Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy met...Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.展开更多
Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categori...Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.展开更多
Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisionin...Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.展开更多
Understanding the variation patterns of tunnel boring machine(TBM)operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels.Studying the mu...Understanding the variation patterns of tunnel boring machine(TBM)operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels.Studying the multifractal characteristics of the TBM oper-ational parameters can help identify the patterns,but the relevant research has not yet been explored.This paper proposed a novel clas-sification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory.Initially,the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored.Subsequently,the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized.The research results showed that the TBM operational parameters of cutterhead torque,total thrust,advance rate,and cutterhead rotation speed have sig-nificant multifractal characteristics.Its multifractal dimension,midpoint slope of the generalized fractal spectrum,and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel.Finally,a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method,and the model was verified through four TBM cycle data containing different surrounding rock grades.The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability.This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.展开更多
对于高维度小样本数据的分类问题,高维属性的复杂性限制了分类模型预测的准确率。为了进一步提高准确率,提出了基于线性回归和属性集成的分类算法。首先,采用线性回归为每一个属性构建属性线性分类器(Attribute Linear Classifier,ALC)...对于高维度小样本数据的分类问题,高维属性的复杂性限制了分类模型预测的准确率。为了进一步提高准确率,提出了基于线性回归和属性集成的分类算法。首先,采用线性回归为每一个属性构建属性线性分类器(Attribute Linear Classifier,ALC);其次,为了避免因ALC数量过多而导致准确率下降,利用经验风险最小化策略中的经验损失值作为评估标准来优选ALC;最后,应用多数投票法来集成被筛选的ALC。采用高维度小样本的基因表达数据集进行实验,结果显示该算法具有比逻辑回归、支持向量机和随机森林算法更高的准确率。展开更多
基金This research was funded by National Natural Science Foundation of China(Nos.31571920,61671378)。
文摘Tegillarca granosa(T.granosa)is susceptible to heavy metals,which may pose a threat to consumer health.Thus,healthy and polluted T.granosa should be distinguished quickly.This study aimed to rapidly identify heavy metal pollution by using laser-induced breakdown spectroscopy(LIBS)coupled with linear regression classification(LRC).Five types of T.granosa were studied,namely,Cd-,Zn-,Pb-contaminated,mixed contaminated,and control samples.Threshold method was applied to extract the significant variables from LIBS spectra.Then,LRC was used to classify the different types of T.granosa.Other classification models and feature selection methods were used for comparison.LRC was the best model,achieving an accuracy of 90.67%.Results indicated that LIBS combined with LRC is effective and feasible for T.granosa heavy metal detection.
基金supported by the Natural Science Foundation of Zhejiang Province(No.LY21C200001)National Natural Science Foundation of China(No.31571920)+1 种基金Wenzhou Science and Technology Project(No.N20160004)Wenzhou Basic Public Welfare Project(No.N20190017)。
文摘Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.
文摘Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and U19A20111).
文摘Understanding the variation patterns of tunnel boring machine(TBM)operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels.Studying the multifractal characteristics of the TBM oper-ational parameters can help identify the patterns,but the relevant research has not yet been explored.This paper proposed a novel clas-sification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory.Initially,the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored.Subsequently,the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized.The research results showed that the TBM operational parameters of cutterhead torque,total thrust,advance rate,and cutterhead rotation speed have sig-nificant multifractal characteristics.Its multifractal dimension,midpoint slope of the generalized fractal spectrum,and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel.Finally,a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method,and the model was verified through four TBM cycle data containing different surrounding rock grades.The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability.This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.
文摘对于高维度小样本数据的分类问题,高维属性的复杂性限制了分类模型预测的准确率。为了进一步提高准确率,提出了基于线性回归和属性集成的分类算法。首先,采用线性回归为每一个属性构建属性线性分类器(Attribute Linear Classifier,ALC);其次,为了避免因ALC数量过多而导致准确率下降,利用经验风险最小化策略中的经验损失值作为评估标准来优选ALC;最后,应用多数投票法来集成被筛选的ALC。采用高维度小样本的基因表达数据集进行实验,结果显示该算法具有比逻辑回归、支持向量机和随机森林算法更高的准确率。
