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
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
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
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
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations im...The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.展开更多
As the“engine”of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the pl...As the“engine”of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation(FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85%of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.展开更多
In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusi...In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.展开更多
Evaluating the consistency of herb injectable formulations could improve their product quality and clinical safety,particularly concerning the composition and content levels of trace ingredients.Panax notoginseng Sapo...Evaluating the consistency of herb injectable formulations could improve their product quality and clinical safety,particularly concerning the composition and content levels of trace ingredients.Panax notoginseng Saponins Injection(PNSI),widely used in China for treating acute cardiovascular diseases,contains low-abundance(10%-25%)and trace saponins in addition to its five main constituents(notoginsenoside R1,ginsenoside Rg1,ginsenoside Re,ginsenoside Rb1,and ginsenoside Rd).This study aimed to establish a robust analytical method and assess the variability in trace saponin levels within PNSI from different vendors and formulation types.To achieve this,a liquid chromatography-triple quadrupole mass spectrometry(LC-MS/MS)method employing multiple ions monitoring(MIM)was developed.A“post-column valve switching”strategy was implemented to eliminate highly abundant peaks(NR_(1),Rg_(1),and Re)at 26 min.A total of 51 saponins in PNSI were quantified or relatively quantified using 18 saponin standards,with digoxin as the internal standard.This study evaluated 119 batches of PNSI from seven vendors,revealing significant variability in trace saponin levels among different vendors and formulation types.These findings highlight the importance of consistent content in low-abundance and trace saponins to ensure product control and clinical safety.Standardization of these ingredients is crucial for maintaining the quality and effectiveness of PNSI in treating acute cardiovascular diseases.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
This study highlights a new by-product source for cobalt by recycling Paleoproterozoic Mn deposits.We present a geochemical modeling approach utilizing Principal Component Analysis(PCA)for available geochemical data o...This study highlights a new by-product source for cobalt by recycling Paleoproterozoic Mn deposits.We present a geochemical modeling approach utilizing Principal Component Analysis(PCA)for available geochemical data of Paleoproterozoic manganese deposits found in Africa and Brazil,which exhibit anomalous cobalt contents(up to 1200 ppm)along with other metals such as copper,nickel,and vanadium.The PCA results for the correlation coefficient matrix of the Enrichment Factor(_(EF))values of major and trace elements from samples of eight Mn deposits found in Africa and Brazil(Kisenge-Kamata,Moanda,Nsuta in Africa,and Azul,Buritirama,Lagoa do Riacho,Morro da Mina,and Serra do Navio in Brazil)yielded a cumulative variance of 53.3%for PC1(34%)and PC2(19.3%).In PC1,the highest positive loadings correspond to the variables Mn_(EF),Ni_(EF),and Co_(EF),while the highest negative loadings correspond to the variables Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF).PC2 exhibits the highest positive loadings for the variables Ca_(_(EF)),Mg_(EF),and P_(EF),while the highest negative loadings are for Cu_(EF)and V_(EF).The biplot diagram representation showed that clusters of vectors Mn_(EF),Ni_(EF),Co_(EF),V_(EF),and Cu_(EF)influence samples of Mn-carbonate rock,Mn-carbonate-silicate rock,Mn-silicate rock,and Mn-carbonate-siliciclastic rock,all with high Co_(EF)values(up to 414).The cluster of vectors Ca_(_(EF)),Mg_(EF),and P_(EF)significantly influence carbonate rock and dolomite marble,which have low Co_(EF)values(close to 0).The cluster of vectors Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF)strongly influences siliciclastic rock,which exhibits low Co_(EF)values.On the other hand,the cluster of vectors Cu_(EF)and V_(EF)influences oxidized Mn ore,which exhibits Co_(EF)values of up to 108.The results reveal a dichotomy regarding the origin of cobalt and other metal enrichments in these deposits linked to the Mn redox cycle.This process involves the formation of Mn-oxyhydroxides with the adsorption of Co and other metals under oxic conditions,followed by the burial of these Mn oxides in an anoxic diagenetic environment,where microbial sulfate reduction leads to the nucleation of Mn-carbonates and the formation of metal-rich sulfides(Fe,Co,Ni,V).Additionally,detrital input and sulfide phases(e.g.,framboidal pyrite)for the formation of Mn-rich siliciclastic rocks associated with Mn-carbonate rocks are evidenced by proxies Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF).This new exploration approach,supported by geochemical modeling through PCA,enhances our understanding of the genesis of these Paleoproterozoic manganese deposits and highlights a new route for cobalt exploration.In the increasing global demand for cobalt,particularly in applications involving electric vehicle batteries and energy storage,exploring these deposits emerges as an alternative source to produce these critical metals.展开更多
To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target...To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.展开更多
Understanding the ecological evolution is of great significance in addressing the impacts of climate change and human activities.However,the ecological evolution and its drivers remain inadequately explored in arid an...Understanding the ecological evolution is of great significance in addressing the impacts of climate change and human activities.However,the ecological evolution and its drivers remain inadequately explored in arid and semi-arid areas.This study took the Helan Mountain,a typical arid and semi-arid area in China,as the study area.By adopting an Enhanced Remote Sensing Ecological Index(ERSEI)that integrates the habitat quality(HQ)index with the Remote Sensing Ecological Index(RSEI),we quantified the ecological environment quality of the Helan Mountain during 2010-2022 and analyzed the driving factors behind the changes.Principal Component Analysis(PCA)was used to validate the composite ERSEI,enabling the extraction of key features and the reduction of redundant information.The results showed that the contributions of first principal component(PC1)for ERSEI and RSEI were 80.23%and 78.72%,respectively,indicating that the ERSEI can provide higher precision and more details than the RSEI in assessing ecological environment quality.Temporally,the ERSEI in the Helan Mountain exhibited an initial decline followed by an increase from 2010 to 2022,with the average value of ERSEI ranging between 0.298 and 0.346.Spatially,the ERSEI showed a trend of being higher in the southwest and lower in the northeast,with high-quality ecological environments mainly concentrated in the western foothills at higher altitudes.The centroid of ERSEI shifted northeastward toward Helan County from 2010 to 2022.Temperature and digital elevation model(DEM)emerged as the primary drivers of ERSEI changes.This study highlights the necessity of using comprehensive monitoring tools to guide policy-making and conservation strategies,ensuring the resilience of fragile ecosystems in the face of ongoing climatic and anthropogenic pressures.The findings offer valuable insights for the sustainable management and conservation in arid and semi-arid ecosystems.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 anal...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
In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang ...In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang Plain, Northeast China. The basic principle of the PCA is that the pairs of parameters which are highly correlated and linear with each other would contribute the same information to an assessment and one of them should be eliminated. The method of the PCA is that a correlation relationship among candidate parameters is examined before the FSE. If there is an apparent nonlinear or curvilinear relationship between two parameters, then both will be retained; if the correlation is significant (p<0.01), and the scatter plot suggests a linear relationship, then one of them will be deleted. However, which one will be deleted? For solving this problem, a sensitivity test was conducted and the higher sensitivity parameters remained. The results indicate that the original data should be preprocessed through the PCA for redundancy and variability. The study shows that introducing the PCA into the FSE can simplify the FSE calculation process greatly, while the results have not been changed much.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS trans...This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.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
Drought stress is one of the major constraints to rice （Oryza sativa L.） production and yield stability especially in rainfed ecosystems and is getting worse as the climate changes worldwide. Dongxiang wild rice （D...Drought stress is one of the major constraints to rice （Oryza sativa L.） production and yield stability especially in rainfed ecosystems and is getting worse as the climate changes worldwide. Dongxiang wild rice （DXWR） Oryza rufipogon Griff., contains drought resistant gene. Improving drought resistance of cultivars is crucial to increase and stabilize rice grain yield via transferring resistant gene from species related to rice. In this paper, four upland rice, sixty backcross inbred lines （BILs） derived from BC1F5 of R974//DXWR/R974, and their parents were employed to evaluate drought-resistance at seedling stage in the greenhouse. Nine traits were recorded for assessment of drought resistance, including maximum root length （MRL）, number of roots （NR）, shoot length （SL）, dry root weight （DRW）, fresh root weight （FRW）, root relative water content （RRWC）, leaf relative water content （LRWC）, level for rolling leaf （LRL）, and seedling survivability under repeat drought （SSRD）. Using more than 88% of accumulative contribution resulted from the principal component analysis （PCA）, the nine traits were classified into five independent principal components and the line 1949 showed the highest resistance. Analysis on the stepwise regression equation and correlation demonstrated that MRL, RN, FRW, and RRWC significantly influenced the drought resistance, thus could be used as comprehensive index for drought resistance at the seedling stage. Using the major gene plus polygene mixed inheritance model of quantitative traits, the inheritance of drought-resistance of BIL population at seedling stage was mostly controlled by two independent genes plus polygene. As a result, the DXWR could be precious resources for genetic improvement of drought resistance in cultivated rice.展开更多
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.
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.
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.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
文摘The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.
文摘As the“engine”of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation(FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85%of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.
基金Open Fund Project of Key Laboratory of Instrumentation Science&Dynamic Measurement(No.2DSYSJ2015005)Specialized Research Fund for the Doctoral Program of Ministry of Education Colleges(No.20121420110004)
文摘In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.
基金supported by the Science and Technology Service Network Initiative of the Chinese Academy of Sciences(STS,No.KFJ-STS-QYZD-2021-03-003)the Construction Projects of the Research Center for Notoginseng Health Products by the Department of Science and Technology of Guangxi Province(No.AD20297068)the Sanming Project of Medicine in Shenzhen(No.SZZYSM202106004).
文摘Evaluating the consistency of herb injectable formulations could improve their product quality and clinical safety,particularly concerning the composition and content levels of trace ingredients.Panax notoginseng Saponins Injection(PNSI),widely used in China for treating acute cardiovascular diseases,contains low-abundance(10%-25%)and trace saponins in addition to its five main constituents(notoginsenoside R1,ginsenoside Rg1,ginsenoside Re,ginsenoside Rb1,and ginsenoside Rd).This study aimed to establish a robust analytical method and assess the variability in trace saponin levels within PNSI from different vendors and formulation types.To achieve this,a liquid chromatography-triple quadrupole mass spectrometry(LC-MS/MS)method employing multiple ions monitoring(MIM)was developed.A“post-column valve switching”strategy was implemented to eliminate highly abundant peaks(NR_(1),Rg_(1),and Re)at 26 min.A total of 51 saponins in PNSI were quantified or relatively quantified using 18 saponin standards,with digoxin as the internal standard.This study evaluated 119 batches of PNSI from seven vendors,revealing significant variability in trace saponin levels among different vendors and formulation types.These findings highlight the importance of consistent content in low-abundance and trace saponins to ensure product control and clinical safety.Standardization of these ingredients is crucial for maintaining the quality and effectiveness of PNSI in treating acute cardiovascular diseases.
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the Department of Geology at the Federal University of Ceará(DEGEO-UFC)and Prof.Dr.Felipe Holanda dos Santos for their support and guidance during the research process.FHS would like to thank the Society of Economic Geologists(SEG)for the Student Research Grant.WAS is funded by CNPq,under grant number(407255/2022-2).
文摘This study highlights a new by-product source for cobalt by recycling Paleoproterozoic Mn deposits.We present a geochemical modeling approach utilizing Principal Component Analysis(PCA)for available geochemical data of Paleoproterozoic manganese deposits found in Africa and Brazil,which exhibit anomalous cobalt contents(up to 1200 ppm)along with other metals such as copper,nickel,and vanadium.The PCA results for the correlation coefficient matrix of the Enrichment Factor(_(EF))values of major and trace elements from samples of eight Mn deposits found in Africa and Brazil(Kisenge-Kamata,Moanda,Nsuta in Africa,and Azul,Buritirama,Lagoa do Riacho,Morro da Mina,and Serra do Navio in Brazil)yielded a cumulative variance of 53.3%for PC1(34%)and PC2(19.3%).In PC1,the highest positive loadings correspond to the variables Mn_(EF),Ni_(EF),and Co_(EF),while the highest negative loadings correspond to the variables Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF).PC2 exhibits the highest positive loadings for the variables Ca_(_(EF)),Mg_(EF),and P_(EF),while the highest negative loadings are for Cu_(EF)and V_(EF).The biplot diagram representation showed that clusters of vectors Mn_(EF),Ni_(EF),Co_(EF),V_(EF),and Cu_(EF)influence samples of Mn-carbonate rock,Mn-carbonate-silicate rock,Mn-silicate rock,and Mn-carbonate-siliciclastic rock,all with high Co_(EF)values(up to 414).The cluster of vectors Ca_(_(EF)),Mg_(EF),and P_(EF)significantly influence carbonate rock and dolomite marble,which have low Co_(EF)values(close to 0).The cluster of vectors Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF)strongly influences siliciclastic rock,which exhibits low Co_(EF)values.On the other hand,the cluster of vectors Cu_(EF)and V_(EF)influences oxidized Mn ore,which exhibits Co_(EF)values of up to 108.The results reveal a dichotomy regarding the origin of cobalt and other metal enrichments in these deposits linked to the Mn redox cycle.This process involves the formation of Mn-oxyhydroxides with the adsorption of Co and other metals under oxic conditions,followed by the burial of these Mn oxides in an anoxic diagenetic environment,where microbial sulfate reduction leads to the nucleation of Mn-carbonates and the formation of metal-rich sulfides(Fe,Co,Ni,V).Additionally,detrital input and sulfide phases(e.g.,framboidal pyrite)for the formation of Mn-rich siliciclastic rocks associated with Mn-carbonate rocks are evidenced by proxies Si_(EF),Fe_(EF),K_(EF),Ti_(EF),Cr_(EF),and Zr_(EF).This new exploration approach,supported by geochemical modeling through PCA,enhances our understanding of the genesis of these Paleoproterozoic manganese deposits and highlights a new route for cobalt exploration.In the increasing global demand for cobalt,particularly in applications involving electric vehicle batteries and energy storage,exploring these deposits emerges as an alternative source to produce these critical metals.
基金Defense Industrial Technology Development Program (JCKY2020204B016)National Natural Science Foundation of China (92471206)。
文摘To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
基金funded by the Fujian Province's Foreign Cooperation Project in 2023(2023I0047)the Fujian Provincial Natural Science Foundation Project(2023J011432,2024J011195)+3 种基金the Ministry of Education's Supply-demand Docking Employment and Education Project(2024011223947)the Open Project Fund of Hunan Provincial Key Laboratory for Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area(DTH Key Lab.2024-04,2022-04)the Fujian Provincial Natural Science Foundation Guiding Project(2024Y0057)the Fujian Province Social Science Plan Project(FJ2024BF071).
文摘Understanding the ecological evolution is of great significance in addressing the impacts of climate change and human activities.However,the ecological evolution and its drivers remain inadequately explored in arid and semi-arid areas.This study took the Helan Mountain,a typical arid and semi-arid area in China,as the study area.By adopting an Enhanced Remote Sensing Ecological Index(ERSEI)that integrates the habitat quality(HQ)index with the Remote Sensing Ecological Index(RSEI),we quantified the ecological environment quality of the Helan Mountain during 2010-2022 and analyzed the driving factors behind the changes.Principal Component Analysis(PCA)was used to validate the composite ERSEI,enabling the extraction of key features and the reduction of redundant information.The results showed that the contributions of first principal component(PC1)for ERSEI and RSEI were 80.23%and 78.72%,respectively,indicating that the ERSEI can provide higher precision and more details than the RSEI in assessing ecological environment quality.Temporally,the ERSEI in the Helan Mountain exhibited an initial decline followed by an increase from 2010 to 2022,with the average value of ERSEI ranging between 0.298 and 0.346.Spatially,the ERSEI showed a trend of being higher in the southwest and lower in the northeast,with high-quality ecological environments mainly concentrated in the western foothills at higher altitudes.The centroid of ERSEI shifted northeastward toward Helan County from 2010 to 2022.Temperature and digital elevation model(DEM)emerged as the primary drivers of ERSEI changes.This study highlights the necessity of using comprehensive monitoring tools to guide policy-making and conservation strategies,ensuring the resilience of fragile ecosystems in the face of ongoing climatic and anthropogenic pressures.The findings offer valuable insights for the sustainable management and conservation in arid and semi-arid ecosystems.
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.
基金Under the auspices of National Natural Science Foundation of China (No. 40830535)Knowledge Innovation Pro-gram of Chinese Academy of Sciences (No.KSCX2-YW-N-46-06)
文摘In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang Plain, Northeast China. The basic principle of the PCA is that the pairs of parameters which are highly correlated and linear with each other would contribute the same information to an assessment and one of them should be eliminated. The method of the PCA is that a correlation relationship among candidate parameters is examined before the FSE. If there is an apparent nonlinear or curvilinear relationship between two parameters, then both will be retained; if the correlation is significant (p<0.01), and the scatter plot suggests a linear relationship, then one of them will be deleted. However, which one will be deleted? For solving this problem, a sensitivity test was conducted and the higher sensitivity parameters remained. The results indicate that the original data should be preprocessed through the PCA for redundancy and variability. The study shows that introducing the PCA into the FSE can simplify the FSE calculation process greatly, while the results have not been changed much.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
文摘This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.
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supported by the National Natural Science Fundation of China (30960189)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education of China,the project for Principle Research Topic of Jiangxi Education,China(GJJ08146)+3 种基金the Jiangxi Province Project for Principle Research Leader,China (020007)the Jiangxi Province Inviting Tender Project for Principle ResearchTopic,China (20068)the Natural Science Foundation of Jiangxi Province,China (2009GQN0068)the Youth Foundation of Jiangxi Academy of Agricultural Sciences,China (2010CQN008)
文摘Drought stress is one of the major constraints to rice （Oryza sativa L.） production and yield stability especially in rainfed ecosystems and is getting worse as the climate changes worldwide. Dongxiang wild rice （DXWR） Oryza rufipogon Griff., contains drought resistant gene. Improving drought resistance of cultivars is crucial to increase and stabilize rice grain yield via transferring resistant gene from species related to rice. In this paper, four upland rice, sixty backcross inbred lines （BILs） derived from BC1F5 of R974//DXWR/R974, and their parents were employed to evaluate drought-resistance at seedling stage in the greenhouse. Nine traits were recorded for assessment of drought resistance, including maximum root length （MRL）, number of roots （NR）, shoot length （SL）, dry root weight （DRW）, fresh root weight （FRW）, root relative water content （RRWC）, leaf relative water content （LRWC）, level for rolling leaf （LRL）, and seedling survivability under repeat drought （SSRD）. Using more than 88% of accumulative contribution resulted from the principal component analysis （PCA）, the nine traits were classified into five independent principal components and the line 1949 showed the highest resistance. Analysis on the stepwise regression equation and correlation demonstrated that MRL, RN, FRW, and RRWC significantly influenced the drought resistance, thus could be used as comprehensive index for drought resistance at the seedling stage. Using the major gene plus polygene mixed inheritance model of quantitative traits, the inheritance of drought-resistance of BIL population at seedling stage was mostly controlled by two independent genes plus polygene. As a result, the DXWR could be precious resources for genetic improvement of drought resistance in cultivated rice.
