To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establis...To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage.The hidden layer of the DBN is trained through a layer-by-layer pre-training.Finally,the backpropagation algorithm is used to fine-tune the entire network.The method is validated using a numerical model of a steel truss bridge.The results show that under the influence of noise and modeling uncertainty,the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.展开更多
Objectives:Death fear is the main subject in thanatology.Several researchers have defined different reasons for fear of death.This study aimed to explore the performance of the Farsi version of the Reasons for Death F...Objectives:Death fear is the main subject in thanatology.Several researchers have defined different reasons for fear of death.This study aimed to explore the performance of the Farsi version of the Reasons for Death Fear Scale(RDFS)among a convenience sample of Iranian nurses(n=106).Methods:The nurses were selected by the convenience sampling method and were asked to complete the RDFS,Death Concern Scale,Collett-Lester Fear of Death Scale,Death Anxiety Scale,Death Depression Scale,and Death Obsession Scale.Results:For the RDFS,the Cronbach's a coefficient was 0.90,and the 2-week test-retest reliability was 0.64.The RDFS was correlated at 0.34,0.39,0.50,0.35,and 0.39 to the above-mentioned five scales,indicating its good construct and criterion-related validity.Based on the exploratory factor analysis,the RDFS-identified four factors accounted for 66.20%of the variance and were labeled as"Fear of Pain and Punishment,""Fear of Losing Worldly Involvements,""Religious Transgressions and Failures,"and"Parting from Loved Ones."Conclusions:The RDFS presents good validity and reliability and can be used in clinical and research settings in Iran.展开更多
Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The lin...Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The linear relation between Shannon’s information measures and some signed measure space by using the formal symbols substitution rule is discussed. Furthermore, the coefficient matrix recurrent formula of the linear relation is obtained. Then the coefficient matrix is proved to be invertible via mathematical induction. This shows that the linear relation is one-to-one, and according to this, it can be concluded that a compact space can be generated from Shannon’s information measures.展开更多
基金The National Natural Science Foundation of China(No.51378104)。
文摘To improve the accuracy and anti-noise ability of the structural damage identification method,a bridge damage identification method is proposed based on a deep belief network(DBN).The output vector is used to establish the nonlinear mapping relationship between the mode shape and structural damage.The hidden layer of the DBN is trained through a layer-by-layer pre-training.Finally,the backpropagation algorithm is used to fine-tune the entire network.The method is validated using a numerical model of a steel truss bridge.The results show that under the influence of noise and modeling uncertainty,the damage identification method based on the DBN can identify the accurate damage location and degree identification compared with the traditional damage identification method based on an artificial neural network.
文摘Objectives:Death fear is the main subject in thanatology.Several researchers have defined different reasons for fear of death.This study aimed to explore the performance of the Farsi version of the Reasons for Death Fear Scale(RDFS)among a convenience sample of Iranian nurses(n=106).Methods:The nurses were selected by the convenience sampling method and were asked to complete the RDFS,Death Concern Scale,Collett-Lester Fear of Death Scale,Death Anxiety Scale,Death Depression Scale,and Death Obsession Scale.Results:For the RDFS,the Cronbach's a coefficient was 0.90,and the 2-week test-retest reliability was 0.64.The RDFS was correlated at 0.34,0.39,0.50,0.35,and 0.39 to the above-mentioned five scales,indicating its good construct and criterion-related validity.Based on the exploratory factor analysis,the RDFS-identified four factors accounted for 66.20%of the variance and were labeled as"Fear of Pain and Punishment,""Fear of Losing Worldly Involvements,""Religious Transgressions and Failures,"and"Parting from Loved Ones."Conclusions:The RDFS presents good validity and reliability and can be used in clinical and research settings in Iran.
基金the Science and Technology Research Project of Education Department, Heilongjiang Province (Grant No.11513095)the Science andTechnology Foundation of Heilongjiang Institute of Science and Technology(Grant No.04 -25).
文摘Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The linear relation between Shannon’s information measures and some signed measure space by using the formal symbols substitution rule is discussed. Furthermore, the coefficient matrix recurrent formula of the linear relation is obtained. Then the coefficient matrix is proved to be invertible via mathematical induction. This shows that the linear relation is one-to-one, and according to this, it can be concluded that a compact space can be generated from Shannon’s information measures.
