This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using...This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.展开更多
Extracting characteristic brain signals and simultaneous recording animals behaving could help us to understand the complex behavior of neuronal ensembles. Here, a system was established to record local field potentia...Extracting characteristic brain signals and simultaneous recording animals behaving could help us to understand the complex behavior of neuronal ensembles. Here, a system was established to record local field potentials (LFP) and extracellular signal or multiple-unit discharge and behavior synchronously by utilizing electrophysiology and integrated circuit technique. It comprised microelectrodes and micro-driver assembly, analog front end (AFE),while a computer (Pentium III ) was used as the platform for the graphic user interface, which was developed using the LabVIEW programming language. It was designed as a part of ongoing research to develop a portable wireless neural signal recording system. We believe that this information will be useful for the research of brain-computer interface.展开更多
利用DOE(design of experiment)实验设计方法,研究了无机盐、纤维原料配比和pH对纸料动电位(zeta电位)的影响。结果表明:随着无机盐离子浓度的增加,系统zeta电位增加;与Ca2+、Na+相比,Al3+对纸料系统ze-ta电位的影响程度更为显著。随着...利用DOE(design of experiment)实验设计方法,研究了无机盐、纤维原料配比和pH对纸料动电位(zeta电位)的影响。结果表明:随着无机盐离子浓度的增加,系统zeta电位增加;与Ca2+、Na+相比,Al3+对纸料系统ze-ta电位的影响程度更为显著。随着纸料配比中阔叶浆用量增加,系统zeta电位降低;pH增加也使系统zeta电位降低。当系统zeta电位接近于零时纸料在网部的单程留着率达到最大值。展开更多
基金Project(BK20160685)supported by the Science Foundation of Jiangsu Province,ChinaProject(61620106002)supported by the National Natural Science Foundation of China
文摘This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.
基金Shandong Science Development FundGrant number:041120101
文摘Extracting characteristic brain signals and simultaneous recording animals behaving could help us to understand the complex behavior of neuronal ensembles. Here, a system was established to record local field potentials (LFP) and extracellular signal or multiple-unit discharge and behavior synchronously by utilizing electrophysiology and integrated circuit technique. It comprised microelectrodes and micro-driver assembly, analog front end (AFE),while a computer (Pentium III ) was used as the platform for the graphic user interface, which was developed using the LabVIEW programming language. It was designed as a part of ongoing research to develop a portable wireless neural signal recording system. We believe that this information will be useful for the research of brain-computer interface.
文摘利用DOE(design of experiment)实验设计方法,研究了无机盐、纤维原料配比和pH对纸料动电位(zeta电位)的影响。结果表明:随着无机盐离子浓度的增加,系统zeta电位增加;与Ca2+、Na+相比,Al3+对纸料系统ze-ta电位的影响程度更为显著。随着纸料配比中阔叶浆用量增加,系统zeta电位降低;pH增加也使系统zeta电位降低。当系统zeta电位接近于零时纸料在网部的单程留着率达到最大值。
