In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicoche...In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).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
We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were use...We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Anal...Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.展开更多
This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among ...This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.展开更多
The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of build...The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.展开更多
为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)...为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。展开更多
基金This work was financially supported by the Science and Technology Research Project of Jiangxi Provincial Education Department(GJJ210322)the National Natural Science Foundation of China(No.32260635).
文摘In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).
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We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
文摘Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.
文摘This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.
基金Supported by Scientific Research Project for Commonwealth (GYHY200806017)Innovation Project for Graduate of Jiangsu Province (CX09S-018Z)
文摘The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.
文摘为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。