This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index ...This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index （LMDI） technique was applied with period-wise analysis and time-series analysis. Changes in energy- related CO2 emissions were decomposed into four factors： emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consump- tion is the largest contributor to the decrease in C02 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to C02 emissions, respectively. Policy implications are provided regarding the reduction of C02 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.展开更多
In this paper,we apply a new modification of the Adomian decomposition method for solving the problem of boundary layer convective heat transfer with viscous dissipation and low pressure gradient over a at plate.The t...In this paper,we apply a new modification of the Adomian decomposition method for solving the problem of boundary layer convective heat transfer with viscous dissipation and low pressure gradient over a at plate.The technique is based on the standard Adomian decomposition method and the Chebyshev pseudospectral method.Comparisons are made between the pro-posed technique,the standard Adomian decomposition method,and the numerical solutions to demonstrate the applicability,validity,and high accuracy of the present approach.The results demonstrate that the new modification is more efficient and converges faster than the Adomian decomposition method.展开更多
Model Set Adaptation(MSA)plays a key role in the Variable Structure Multi-Model tracking approach(VSMM).In this paper,the Error-Ambiguity Decomposition(EAD)principle is adopted to derive the EAD-MSA criterion that is ...Model Set Adaptation(MSA)plays a key role in the Variable Structure Multi-Model tracking approach(VSMM).In this paper,the Error-Ambiguity Decomposition(EAD)principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth.Consequently,the EAD Variable Structure first-order General Pseudo Bayesian(EAD-VSGPB1)algorithm and the EAD Variable Structure Interacting Multiple Model(EAD-VSIMM)algorithm are constructed.The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions.The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that,compared to some existing multi-model algorithms,the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.展开更多
文摘This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index （LMDI） technique was applied with period-wise analysis and time-series analysis. Changes in energy- related CO2 emissions were decomposed into four factors： emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consump- tion is the largest contributor to the decrease in C02 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to C02 emissions, respectively. Policy implications are provided regarding the reduction of C02 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.
文摘In this paper,we apply a new modification of the Adomian decomposition method for solving the problem of boundary layer convective heat transfer with viscous dissipation and low pressure gradient over a at plate.The technique is based on the standard Adomian decomposition method and the Chebyshev pseudospectral method.Comparisons are made between the pro-posed technique,the standard Adomian decomposition method,and the numerical solutions to demonstrate the applicability,validity,and high accuracy of the present approach.The results demonstrate that the new modification is more efficient and converges faster than the Adomian decomposition method.
基金funded by the National Natural Science Foundation of China(Nos.61703128,61871166,61701148,61703131)the Science and Technology on Near-Surface Detection Laboratory Foundation,China(No.6142414180208)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F010002)。
文摘Model Set Adaptation(MSA)plays a key role in the Variable Structure Multi-Model tracking approach(VSMM).In this paper,the Error-Ambiguity Decomposition(EAD)principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth.Consequently,the EAD Variable Structure first-order General Pseudo Bayesian(EAD-VSGPB1)algorithm and the EAD Variable Structure Interacting Multiple Model(EAD-VSIMM)algorithm are constructed.The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions.The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that,compared to some existing multi-model algorithms,the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.
文摘静电层析成像(Electrostatic tomography,EST)技术因其无辐射、非入侵、可视化、实时性高、成本低等优势在滑油磨粒在线监测中获得广泛研究,但实际测量中静电信号幅值微弱且含有大量噪声,严重影响图像重建质量。针对上述问题,本文提出一种基于稀疏分解的静电信号降噪方法。首先,对EST传感器测量得到的静电信号数据构建相应的字典,然后用正交匹配追踪(OMP)算法在字典中寻找信号的稀疏表示矩阵,并用其与字典的乘积来表示信号,最后将稀疏表示后的信号代入基于原始对偶内点法(PDIPA)的EST图像重建,并与两种经典降噪方法进行对比。实验结果表明:经过数据降噪处理,重建图像误差相对于降噪前下降5.5%,相对于小波分析或经验模态分解(Empirical Mode De composition,EMD)方法具有较高的准确性;采用本文提出的降噪方法,明显提高了管道内单个荷电颗粒和两个荷电颗粒在不同径向位置时的成像质量。
