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
Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractic...Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.展开更多
Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study co...Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.展开更多
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat...On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.展开更多
To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform(CCT)and principal component analysis(PCA)is p...To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform(CCT)and principal component analysis(PCA)is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavelet low-frequency component with PCA(WLPCA),the method combining contourlet transform with PCA(CPCA),and the method combining wavelet low-frequency and highfrequency components with PCA(WPCA),the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.展开更多
The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third...The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third dimensionality recognition.In this paper,combined with the actual triple star orbits,a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis(PCA)is presented.Firstly,interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly,as a method with simple principle and fast calculation,the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally,the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49%and random noise introduced by the receiver.Meanwhile,due to the influence of orbit distribution of the actual triple star orbits,the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.展开更多
The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary met...The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.展开更多
Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the ta...Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.展开更多
There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it de...There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.展开更多
Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hun...Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.展开更多
[Objectives]To compare the effects of molecular distillation on the flavor and antitumor activity of Ganoderma lucidum spore oil.[Methods]G.lucidum spore oil was separated and purified by molecular distillation techno...[Objectives]To compare the effects of molecular distillation on the flavor and antitumor activity of Ganoderma lucidum spore oil.[Methods]G.lucidum spore oil was separated and purified by molecular distillation technology,and the volatile components of different components of molecular distillation were analyzed by gas chromatography-ion mobility spectrometry(GC-IMS)technology.Human liver carcinoma cells(HepG2),human breast cancer cells(MCF-7),and human cervical cancer cells(Hela)were selected as the tumor cell lines to be tested,and the cell viability was detected by the MTT assay.[Results]Molecular distillation effectively reduced small molecular substances produced by oil oxidation in G.lucidum spore oil,such as heptanal,octanal,linalool,hexanal,E-2-octanal,3-ethylpyridine,etc.Among the heavy components,the content of esters was relatively high,mainly including ethyl levulinate,ethyl crotonate,and amyl butyrate.The MTT cytotoxicity test indicated that G.lucidum spore oil and its molecular distillation components had certain inhibitory effects on the growth of three tumor cells,and G.lucidum spore oil crude oil had the most significant antitumor activity.G.lucidum spore oil crude oil,heavy component,and light component had the most significant antitumor activity on HepG2 cells,followed by MCF-7 cells,and the weakest antitumor activity on Hela cells.The quality of G.lucidum spore oil became higher after molecular distillation,and the rancid smell was reduced,and molecular distillation had little effect on the antitumor activity of G.lucidum spores.[Conclusions]Molecular distillation technology can be applied to the refining of G.lucidum spore oil to improve product quality.展开更多
It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify...It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup> + K<sup>+</sup>, , , Cl<sup>-</sup>, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.展开更多
Rural areas are crucial for a country’s sustainable economy.New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities.This is especially importa...Rural areas are crucial for a country’s sustainable economy.New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities.This is especially important in countries where agricultural production accounts for a significant share of the gross product,such as Russia.In this study,we identified the key indicators of satisfaction and differences between rural and urban citizens based on their social,economic,and environmental backgrounds,and determined whether there are well-being disparities between rural and urban areas in the Stavropol Territory,Russia.We collected primary data through a survey based on the European Social Survey framework to investigate the potential differences between rural and urban areas.By computing the regional well-being index using principal component analysis,we found that there was no statistically significant difference in well-being between rural and urban areas.Results of key indicators showed that rural residents felt psychologically more comfortable and safer,assessed their family relationships better,and adhered more to traditions and customs.However,urban residents showed better economic and social conditions(e.g.,infrastructures,medical care,education,and Internet access).The results of this study imply that we can better understand the local needs,advantages,and unique qualities,thereby gaining insight into the effectiveness of government programs.Policy-makers and local authorities can consider targeted interventions based on the findings of this study and strive to enhance the well-being of both urban and rural residents.展开更多
In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data ...In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.展开更多
Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have ...Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.展开更多
A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we pro...A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.展开更多
A total of 37 elements were determined in tap and bottled water samples from six counties of Middle Tennessee (USA) by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The overarching goal of the st...A total of 37 elements were determined in tap and bottled water samples from six counties of Middle Tennessee (USA) by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The overarching goal of the study is to dispel the myth that bottled water is better than tap water or vice versa. Other parameters analyzed were pH, conductivity, and Total Dissolved Solids (TDS). The results were compared with the Maximum Contaminant Limit (MCL) reported by the US Environmental Protection Agency (US-EPA). The concentrations of phosphorus, silicon, fluoride, and chloride conformed to the established values by US-EPA maximum contaminant level corresponding value. The level of Aluminum (Al), Boron (B), Chromium (Cr), Cobalt (Co), Copper (Cu), Iron (Fe), Lithium (Li), Manganese (Mn), Nickel (Ni), Titanium (Ti), Vanadium (V), and Zinc (Zn) conformed to the established values by governmental agencies (USEPA). Heavy metals such as Arsenic (As), Cadmium (Cd), Cobalt (Co), Lead (Pb), Mercury (Hg), and Silver (Ag) were detected in the tap water of the urban (Davidson) and urbanizing (Rutherford and Williamson) counties;suggesting that rural counties had a less heavy metal concentration in their drinking water sources than urban counties (P < 0.05). However, the values were below the Maximum Contaminant Levels (MCLs).展开更多
The intensification of anthropic uses (i.e., increase of the hemerobic condition) threatens the remnants of native vegetation due to the reduction of its self-regulation capacity. In this research, the Distance to Nat...The intensification of anthropic uses (i.e., increase of the hemerobic condition) threatens the remnants of native vegetation due to the reduction of its self-regulation capacity. In this research, the Distance to Nature (D2N) index for land use and land cover was applied in the Río Grande de Comitán watershed (Southern Mexico) to answer the following questions: 1) What were the land use dynamics observed in the Rio Grande de Comitán watershed in the trajectory through 1999, 2009 and 2019? 2) Does the subcategorization of the D2N allow one to identify which anthropic uses influence more the territorial expression of the watershed? To answer these questions, we performed a supervised classification of land use and land cover was performed in this watershed, and for the D2N index, the classification was simplified to three-category scale for the subcategorization of the anthropic component. Through Principal Component Analysis (PCA), we identified that agricultural anthropogenic use had the greatest influence on territorial expression. The reported scenario indicates a trend of gradual and continuous reduction of naturalness over the last 20 years. Additionally, the D2N index proved to be a useful tool to demonstrate both the anthropic impact, with the simplified scale, and the component that most influences the territory, by subcategorizing the anthropic scale.展开更多
Optimizing the function of ecosystem services(ESs)is vital for implementing regional ecological management strategies.In this study,we used multi-source data and integrated modelling methods to assess the spatiotempor...Optimizing the function of ecosystem services(ESs)is vital for implementing regional ecological management strategies.In this study,we used multi-source data and integrated modelling methods to assess the spatiotemporal variations in eight typical ESs on the Chinese Loess Plateau from 2000 to 2015,including grain production,raw material provision,water conservation,carbon storage service,soil conservation,oxygen production,recreation,and net primary productivity(NPP)services.Then,we divided the ecosystem service bundles(ESBs)according to relationships among the eight ESs,obtaining four types of eco-functional areas at the county(city or banner or district)level based on the spatial clustering of similarities in different ES types.We also identified and assessed the contributions of influencing factors to these eco-functional areas using principal component analysis(PCA)across spatiotemporal scales.We found that the spatiotemporal variations in different ESs were noticeable,with an overall increase in grain production and soil conservation services,no significant change in carbon storage service,and overall decreases in raw material provision,water conservation,oxygen production,recreation,and NPP services.From 2000 to 2015,the number of significant synergistic ES pairs decreased,while that of significant trade-off pairs increased.To the changes of ESBs in the eco-functional areas,the results indicated that the indirect loss of these ESs from forest and grassland due to urban expansion should be reduced in ecological development area(ESB 2)and multi ecological functional area(ESB 3).Meanwhile,crop planting structures and planting densities should be adjusted to reduce ES trade-offs associated with water conservation service in grain-producing area(ESB 4).Lastly,ESB-based ecofunctional zoning can be used to improve ecological restoration management strategies and optimize ecological compensation schemes in ecologically fragile area(ESB 1).展开更多
Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs.This study employed amplified fragment length polymorphism(AF...Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs.This study employed amplified fragment length polymorphism(AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman.Using 12 primer combinations,a total of 1094 bands were scored,of which 1012 were polymorphic.Eighty-two unique markers were identified,which revealed the distinct separation of the seven cultivars.The results obtained show that AFLP can be used to differentiate the banana cultivars.Further classification by phylogenetic,hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis.Based on the analytical results,a consensus dendrogram of the banana cultivars is presented.展开更多
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.
基金Supported by the National Natural Science Foundation of China(No.90818004and61100054)Program for New Century Excellent Talents in University(No.NCET-10-0140)+1 种基金Excellent Youth Foundation of Hunan Scientific Committee(No.11JJ1011)Scientific Research Fundof Hunan Educational Committee(No.09K085and11B048)
文摘Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.
基金Research Project of China Ship Development and Design Center。
文摘Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.
基金National Natural Science Foundation of China(No.60872065)the Key Laboratory of Textile Science&Technology,Ministry of Education,China(No.P1111)+1 种基金the Key Laboratory of Advanced Textile Materials and Manufacturing Technology,Ministry of Education,China(No.2010001)the Priority Academic Program Development of Jiangsu Higher Education Institution,China
文摘To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform(CCT)and principal component analysis(PCA)is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavelet low-frequency component with PCA(WLPCA),the method combining contourlet transform with PCA(CPCA),and the method combining wavelet low-frequency and highfrequency components with PCA(WPCA),the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.
基金This work was supported by the General Design Department,China Academy of Space Technology(10377).
文摘The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third dimensionality recognition.In this paper,combined with the actual triple star orbits,a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis(PCA)is presented.Firstly,interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly,as a method with simple principle and fast calculation,the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally,the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49%and random noise introduced by the receiver.Meanwhile,due to the influence of orbit distribution of the actual triple star orbits,the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.
基金supported by the State Grid Corporation of China“Research and Demonstration on Key Technologies of Distributed Energy Supply System with Complementary Renewable Energy”(No.5230HQ19000J).
文摘The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.
基金Fundamental Research Funds for the Central Universities of Ministry of Education of China。
文摘Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.
文摘There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.
文摘Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.
基金Supported by Taishan Industrial Leading Talent Project(Efficient Ecological Agriculture Innovation)(LJNY202105)。
文摘[Objectives]To compare the effects of molecular distillation on the flavor and antitumor activity of Ganoderma lucidum spore oil.[Methods]G.lucidum spore oil was separated and purified by molecular distillation technology,and the volatile components of different components of molecular distillation were analyzed by gas chromatography-ion mobility spectrometry(GC-IMS)technology.Human liver carcinoma cells(HepG2),human breast cancer cells(MCF-7),and human cervical cancer cells(Hela)were selected as the tumor cell lines to be tested,and the cell viability was detected by the MTT assay.[Results]Molecular distillation effectively reduced small molecular substances produced by oil oxidation in G.lucidum spore oil,such as heptanal,octanal,linalool,hexanal,E-2-octanal,3-ethylpyridine,etc.Among the heavy components,the content of esters was relatively high,mainly including ethyl levulinate,ethyl crotonate,and amyl butyrate.The MTT cytotoxicity test indicated that G.lucidum spore oil and its molecular distillation components had certain inhibitory effects on the growth of three tumor cells,and G.lucidum spore oil crude oil had the most significant antitumor activity.G.lucidum spore oil crude oil,heavy component,and light component had the most significant antitumor activity on HepG2 cells,followed by MCF-7 cells,and the weakest antitumor activity on Hela cells.The quality of G.lucidum spore oil became higher after molecular distillation,and the rancid smell was reduced,and molecular distillation had little effect on the antitumor activity of G.lucidum spores.[Conclusions]Molecular distillation technology can be applied to the refining of G.lucidum spore oil to improve product quality.
文摘It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup> + K<sup>+</sup>, , , Cl<sup>-</sup>, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.
基金supported by the Department of Economics,Faculty of Economics and Management,Czech University of Life Science,Czech(2021B0002).
文摘Rural areas are crucial for a country’s sustainable economy.New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities.This is especially important in countries where agricultural production accounts for a significant share of the gross product,such as Russia.In this study,we identified the key indicators of satisfaction and differences between rural and urban citizens based on their social,economic,and environmental backgrounds,and determined whether there are well-being disparities between rural and urban areas in the Stavropol Territory,Russia.We collected primary data through a survey based on the European Social Survey framework to investigate the potential differences between rural and urban areas.By computing the regional well-being index using principal component analysis,we found that there was no statistically significant difference in well-being between rural and urban areas.Results of key indicators showed that rural residents felt psychologically more comfortable and safer,assessed their family relationships better,and adhered more to traditions and customs.However,urban residents showed better economic and social conditions(e.g.,infrastructures,medical care,education,and Internet access).The results of this study imply that we can better understand the local needs,advantages,and unique qualities,thereby gaining insight into the effectiveness of government programs.Policy-makers and local authorities can consider targeted interventions based on the findings of this study and strive to enhance the well-being of both urban and rural residents.
基金financially supported by the National Natural Science Foundation of China(52174117,52004117)Postdoctoral Science Foundation of China(2021T140290,2020M680975)Science and Technology Research Project of Liaoning Provincial Department of Education(LJ2020JCL005).
文摘In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.
文摘Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.
基金supported by the National Natural Science Foundation of China(6200220861572063+1 种基金61603225)the Natural Science Foundation of Shandong Province(ZR2016FQ04)。
文摘A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.
文摘A total of 37 elements were determined in tap and bottled water samples from six counties of Middle Tennessee (USA) by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The overarching goal of the study is to dispel the myth that bottled water is better than tap water or vice versa. Other parameters analyzed were pH, conductivity, and Total Dissolved Solids (TDS). The results were compared with the Maximum Contaminant Limit (MCL) reported by the US Environmental Protection Agency (US-EPA). The concentrations of phosphorus, silicon, fluoride, and chloride conformed to the established values by US-EPA maximum contaminant level corresponding value. The level of Aluminum (Al), Boron (B), Chromium (Cr), Cobalt (Co), Copper (Cu), Iron (Fe), Lithium (Li), Manganese (Mn), Nickel (Ni), Titanium (Ti), Vanadium (V), and Zinc (Zn) conformed to the established values by governmental agencies (USEPA). Heavy metals such as Arsenic (As), Cadmium (Cd), Cobalt (Co), Lead (Pb), Mercury (Hg), and Silver (Ag) were detected in the tap water of the urban (Davidson) and urbanizing (Rutherford and Williamson) counties;suggesting that rural counties had a less heavy metal concentration in their drinking water sources than urban counties (P < 0.05). However, the values were below the Maximum Contaminant Levels (MCLs).
文摘The intensification of anthropic uses (i.e., increase of the hemerobic condition) threatens the remnants of native vegetation due to the reduction of its self-regulation capacity. In this research, the Distance to Nature (D2N) index for land use and land cover was applied in the Río Grande de Comitán watershed (Southern Mexico) to answer the following questions: 1) What were the land use dynamics observed in the Rio Grande de Comitán watershed in the trajectory through 1999, 2009 and 2019? 2) Does the subcategorization of the D2N allow one to identify which anthropic uses influence more the territorial expression of the watershed? To answer these questions, we performed a supervised classification of land use and land cover was performed in this watershed, and for the D2N index, the classification was simplified to three-category scale for the subcategorization of the anthropic component. Through Principal Component Analysis (PCA), we identified that agricultural anthropogenic use had the greatest influence on territorial expression. The reported scenario indicates a trend of gradual and continuous reduction of naturalness over the last 20 years. Additionally, the D2N index proved to be a useful tool to demonstrate both the anthropic impact, with the simplified scale, and the component that most influences the territory, by subcategorizing the anthropic scale.
基金supported by the Science Foundation of Hubei Province(2021CFB295)the China Postdoctoral Science Foundation(2023M730363)+1 种基金the China Meteorological Administration Key Open Laboratory of Transforming Climate Resources to Economy(2023016)the National Natural Science Foundation of China(42171415)。
文摘Optimizing the function of ecosystem services(ESs)is vital for implementing regional ecological management strategies.In this study,we used multi-source data and integrated modelling methods to assess the spatiotemporal variations in eight typical ESs on the Chinese Loess Plateau from 2000 to 2015,including grain production,raw material provision,water conservation,carbon storage service,soil conservation,oxygen production,recreation,and net primary productivity(NPP)services.Then,we divided the ecosystem service bundles(ESBs)according to relationships among the eight ESs,obtaining four types of eco-functional areas at the county(city or banner or district)level based on the spatial clustering of similarities in different ES types.We also identified and assessed the contributions of influencing factors to these eco-functional areas using principal component analysis(PCA)across spatiotemporal scales.We found that the spatiotemporal variations in different ESs were noticeable,with an overall increase in grain production and soil conservation services,no significant change in carbon storage service,and overall decreases in raw material provision,water conservation,oxygen production,recreation,and NPP services.From 2000 to 2015,the number of significant synergistic ES pairs decreased,while that of significant trade-off pairs increased.To the changes of ESBs in the eco-functional areas,the results indicated that the indirect loss of these ESs from forest and grassland due to urban expansion should be reduced in ecological development area(ESB 2)and multi ecological functional area(ESB 3).Meanwhile,crop planting structures and planting densities should be adjusted to reduce ES trade-offs associated with water conservation service in grain-producing area(ESB 4).Lastly,ESB-based ecofunctional zoning can be used to improve ecological restoration management strategies and optimize ecological compensation schemes in ecologically fragile area(ESB 1).
基金Project supported by Programs of Sultan Qaboos University (Nos SR/AGR/BIOR/05/01 and IG/AGR/PLANT/04/01),Sultanate of Oman,and the Research Chair in Postharvest Technology at the University of Stellenbosch,South Africa
文摘Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs.This study employed amplified fragment length polymorphism(AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman.Using 12 primer combinations,a total of 1094 bands were scored,of which 1012 were polymorphic.Eighty-two unique markers were identified,which revealed the distinct separation of the seven cultivars.The results obtained show that AFLP can be used to differentiate the banana cultivars.Further classification by phylogenetic,hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis.Based on the analytical results,a consensus dendrogram of the banana cultivars is presented.
