The implementation of embedded selective catalytic reduction(SCR)denitration in chain grate during iron ore pelletizing process obviates additional flue gas heating.However,the influence of gas components and alkali m...The implementation of embedded selective catalytic reduction(SCR)denitration in chain grate during iron ore pelletizing process obviates additional flue gas heating.However,the influence of gas components and alkali metal on SCR denitration requires attention.The SCR denitration behavior in the preheating section of chain grate was investigated,and the combined influence mechanisms of H_(2)O(g),SO_(2),and potassium were revealed.The results show that the presence of H_(2)O(g)and SO_(2) in the flue gas decreases the NO conversion rate of the catalyst from 96.3%to 79.5%,while potassium adsorbed on the catalyst surface further reduces the NO conversion rate to 74.1%.H_(2)O(g),SO_(2),and potassium in the flue gas form sulfate and potassium salt on the catalyst surface,blocking the pore structure,thereby decreasing the gas adsorption capacity of the catalyst.Moreover,SO_(2) and potassium engage in competitive adsorption and reaction with NH_(3) and NO at the active sites on the catalyst surface,reducing the content and activity of the catalyst effective component.Increasing the flue gas temperature can promote the decomposition of ammonium sulfate and ammonium bisulfate on the catalyst surface,but it has little effect on potassium.Additionally,potassium will exacerbate sulfur poisoning of the catalyst.Hence,the embedded SCR denitration process requires electrostatic precipitation to eliminate the adverse impacts of potassium and thermal regime optimization to raise flue gas temperature to 350℃,thereby increasing NO conversion rate exceeding 85%.展开更多
Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predomina...Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.展开更多
Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heurist...Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.展开更多
In recent years, online engineering technologies are widely distributed and developed. Their influence on society is very strong. The Internet technology has provided additional opportunities for a new development lev...In recent years, online engineering technologies are widely distributed and developed. Their influence on society is very strong. The Internet technology has provided additional opportunities for a new development level of education, design and production. Associations and scientific conferences in the field of online engineering that appeared, seek to foster practices in education and research in higher education institutions and the industry on online engineering. A particular challenge for online engineering is how to extend the traditional equipments and laboratories to the Internet. A method of the embedded systems design with using online laboratory is described in this paper. Also, in this paper the experimental set of remote laboratory which allows carrying out hardware/software oriented design of the embedded control system of a mobile platform is considered.展开更多
How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle co...How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.展开更多
China’s Dongfang Electric Machinery successfully completed the manufacturing of the first batch of embedded components for six turbines it is supplying for the Koysha Hydroelectric Dam project in Ethiopia.Transported...China’s Dongfang Electric Machinery successfully completed the manufacturing of the first batch of embedded components for six turbines it is supplying for the Koysha Hydroelectric Dam project in Ethiopia.Transported by land and sea,these parts will arrive on site in three months.Once completed,the power plant will generate more than 6 billion kwh of electricity annually,reducing carbon dioxide emissions by 1 million tonnes per year.展开更多
函数型聚类分析在统计学领域被广泛关注,其分析过程通常在降维目标实现后进行。为了有效解决函数型主成分聚类问题,文章结合局部线性嵌入算法(Locally Linear Embedding,LLE)在非线性空间下的适用性,提出了一种局部线性下的函数型主成...函数型聚类分析在统计学领域被广泛关注,其分析过程通常在降维目标实现后进行。为了有效解决函数型主成分聚类问题,文章结合局部线性嵌入算法(Locally Linear Embedding,LLE)在非线性空间下的适用性,提出了一种局部线性下的函数型主成分分析模型(LLE Function Principle Component Analysis,LFPCA)。首先,采用函数型主成分分析法作为降维目标方法,改进了FPCA的算法模型,通过将LLE算法的权重系数矩阵与函数型主成分定义相结合,构建出一个适用于非线性空间下的聚类算法;其次,在求解算法的过程中定义了函数型主成分得分,并结合EM算法构建出GMM模型来近似函数型算法的概率密度函数,使模型更高效且适用性更强;最后,通过随机模拟实验及应用分析验证了LFPCA算法模型在真实数据集上具有良好的聚类效能。展开更多
基金financially supported by the National Key Research and Development Program of China(No.2023YFC3707002)Hunan Provincial Innovation Foundation for Postgraduate(No.QL20220069)Postgraduate Innovative Project of Central South University(No.1053320214756).
文摘The implementation of embedded selective catalytic reduction(SCR)denitration in chain grate during iron ore pelletizing process obviates additional flue gas heating.However,the influence of gas components and alkali metal on SCR denitration requires attention.The SCR denitration behavior in the preheating section of chain grate was investigated,and the combined influence mechanisms of H_(2)O(g),SO_(2),and potassium were revealed.The results show that the presence of H_(2)O(g)and SO_(2) in the flue gas decreases the NO conversion rate of the catalyst from 96.3%to 79.5%,while potassium adsorbed on the catalyst surface further reduces the NO conversion rate to 74.1%.H_(2)O(g),SO_(2),and potassium in the flue gas form sulfate and potassium salt on the catalyst surface,blocking the pore structure,thereby decreasing the gas adsorption capacity of the catalyst.Moreover,SO_(2) and potassium engage in competitive adsorption and reaction with NH_(3) and NO at the active sites on the catalyst surface,reducing the content and activity of the catalyst effective component.Increasing the flue gas temperature can promote the decomposition of ammonium sulfate and ammonium bisulfate on the catalyst surface,but it has little effect on potassium.Additionally,potassium will exacerbate sulfur poisoning of the catalyst.Hence,the embedded SCR denitration process requires electrostatic precipitation to eliminate the adverse impacts of potassium and thermal regime optimization to raise flue gas temperature to 350℃,thereby increasing NO conversion rate exceeding 85%.
基金funded by the National Natural Science Foundation of China Instrumentation Program(52327806)Youth Fund of the National Nature Foundation of China(62405020)China Postdoctoral Science Foundation(2024M764131).
文摘Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.
基金supported in part by the National Natural Science Foundation of China under Grant No.61471055
文摘Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.
文摘In recent years, online engineering technologies are widely distributed and developed. Their influence on society is very strong. The Internet technology has provided additional opportunities for a new development level of education, design and production. Associations and scientific conferences in the field of online engineering that appeared, seek to foster practices in education and research in higher education institutions and the industry on online engineering. A particular challenge for online engineering is how to extend the traditional equipments and laboratories to the Internet. A method of the embedded systems design with using online laboratory is described in this paper. Also, in this paper the experimental set of remote laboratory which allows carrying out hardware/software oriented design of the embedded control system of a mobile platform is considered.
基金National Key Science & Technology Special Projects(Grant No.2008ZX05000-004)CNPC Projects(Grant No.2008E-0610-10).
文摘How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
文摘China’s Dongfang Electric Machinery successfully completed the manufacturing of the first batch of embedded components for six turbines it is supplying for the Koysha Hydroelectric Dam project in Ethiopia.Transported by land and sea,these parts will arrive on site in three months.Once completed,the power plant will generate more than 6 billion kwh of electricity annually,reducing carbon dioxide emissions by 1 million tonnes per year.
文摘函数型聚类分析在统计学领域被广泛关注,其分析过程通常在降维目标实现后进行。为了有效解决函数型主成分聚类问题,文章结合局部线性嵌入算法(Locally Linear Embedding,LLE)在非线性空间下的适用性,提出了一种局部线性下的函数型主成分分析模型(LLE Function Principle Component Analysis,LFPCA)。首先,采用函数型主成分分析法作为降维目标方法,改进了FPCA的算法模型,通过将LLE算法的权重系数矩阵与函数型主成分定义相结合,构建出一个适用于非线性空间下的聚类算法;其次,在求解算法的过程中定义了函数型主成分得分,并结合EM算法构建出GMM模型来近似函数型算法的概率密度函数,使模型更高效且适用性更强;最后,通过随机模拟实验及应用分析验证了LFPCA算法模型在真实数据集上具有良好的聚类效能。
