There are many factors affecting the instability of the submarine hydrate-bearing slope (SHBS),and the interaction with hydrate is very complicated.In this paper,the mechanical mechanism of the static liquefaction and...There are many factors affecting the instability of the submarine hydrate-bearing slope (SHBS),and the interaction with hydrate is very complicated.In this paper,the mechanical mechanism of the static liquefaction and instability of submarine slope caused by the dissociation of natural gas hydrate (NGH) resulting in the rapid increase of pore pressure of gas hydrate-bearing sediments (GHBS) and the decrease of effective stress are analyzed based on the time series and type of SHBS.Then,taking the typical submarine slope in the northern South China Sea as an example,four important factors affecting the stability of SHBS are selected,such as the degree of hydrate dissociation,the depth of hydrate burial,the thickness of hydrate,and the depth of seawater.According to the principle of orthogonal method,25 orthogonal test schemes with 4 factors and 5 levels are designed and the safety factors of submarine slope stability of each scheme are calculated by using the strength reduction finite element method.By means of the orthogonal design range analysis and the variance analysis,sensitivity of influential factors on stability of SHBS are obtained.The results show that the degree of hydrate dissociation is the most sensitive,followed by hydrate burial depth,the thickness of hydrate and the depth of seawater.Finally,the concept of gas hydrate critical burial depth is put forward according to the influence law of gas hydrate burial depth,and the numerical simulation for specific submarine slope is carried out,which indicates the existence of critical burial depth.展开更多
This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local ar...This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.展开更多
The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performanc...The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).展开更多
The impact of excavation on the reliability of anti- pull piles is studied, and three cases of reliability analysis, named reliability of ultimate limit state （ULS）, reliability of serviceability limit state （SLS）...The impact of excavation on the reliability of anti- pull piles is studied, and three cases of reliability analysis, named reliability of ultimate limit state （ULS）, reliability of serviceability limit state （SLS） and reliability of system （SYS） are studied. The reduction factor of the pile capacity is used to calculate the reliability indices for the three cases. The ratio ξ of the pile capacity of SLS to the pile capacity of ULS has a significant influence on the reliability indices of SLS and SYS. The mean value μξ of the ratio ξ： is considered as a random variable to study the reliability indices of SLS and SYS. The numerical example demonstrates that the excavation depth and the excavation diameter are proved to have significant influences on the reduction factor of the pile capacity and the reliability indices. The reliability indices decrease with the increase in the excavation depth, and the excavation diameter has a considerable influence on the reliability index when the excavation is relatively deep. In addition, μξ has a significant influence on the reliability indices of SLS and SYS. For a more accurate estimation of μξ, further research should be conducted to study μξ.展开更多
基金the National Natural Science Foundation of China (11572165)the China Geological Survey (DD20160217).
文摘There are many factors affecting the instability of the submarine hydrate-bearing slope (SHBS),and the interaction with hydrate is very complicated.In this paper,the mechanical mechanism of the static liquefaction and instability of submarine slope caused by the dissociation of natural gas hydrate (NGH) resulting in the rapid increase of pore pressure of gas hydrate-bearing sediments (GHBS) and the decrease of effective stress are analyzed based on the time series and type of SHBS.Then,taking the typical submarine slope in the northern South China Sea as an example,four important factors affecting the stability of SHBS are selected,such as the degree of hydrate dissociation,the depth of hydrate burial,the thickness of hydrate,and the depth of seawater.According to the principle of orthogonal method,25 orthogonal test schemes with 4 factors and 5 levels are designed and the safety factors of submarine slope stability of each scheme are calculated by using the strength reduction finite element method.By means of the orthogonal design range analysis and the variance analysis,sensitivity of influential factors on stability of SHBS are obtained.The results show that the degree of hydrate dissociation is the most sensitive,followed by hydrate burial depth,the thickness of hydrate and the depth of seawater.Finally,the concept of gas hydrate critical burial depth is put forward according to the influence law of gas hydrate burial depth,and the numerical simulation for specific submarine slope is carried out,which indicates the existence of critical burial depth.
文摘This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.
文摘The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).
基金The National Natural Science Foundation of China(No. 50978112)
文摘The impact of excavation on the reliability of anti- pull piles is studied, and three cases of reliability analysis, named reliability of ultimate limit state （ULS）, reliability of serviceability limit state （SLS） and reliability of system （SYS） are studied. The reduction factor of the pile capacity is used to calculate the reliability indices for the three cases. The ratio ξ of the pile capacity of SLS to the pile capacity of ULS has a significant influence on the reliability indices of SLS and SYS. The mean value μξ of the ratio ξ： is considered as a random variable to study the reliability indices of SLS and SYS. The numerical example demonstrates that the excavation depth and the excavation diameter are proved to have significant influences on the reduction factor of the pile capacity and the reliability indices. The reliability indices decrease with the increase in the excavation depth, and the excavation diameter has a considerable influence on the reliability index when the excavation is relatively deep. In addition, μξ has a significant influence on the reliability indices of SLS and SYS. For a more accurate estimation of μξ, further research should be conducted to study μξ.
