Conventional farming-pastoral ecotones methods of delineating were not quantitative and could not fully show their spatial distribution. The present paper attempts to develop quantitative methods for mapping farming-...Conventional farming-pastoral ecotones methods of delineating were not quantitative and could not fully show their spatial distribution. The present paper attempts to develop quantitative methods for mapping farming-pastoral ecotones in China. Nine indicators, related to temperature, precipitation and altitude aspects, were selected to quantify ecological susceptibility of vegetation （crops and forage）. Methods of analytic hierarchy process （AHP） and expert score ranking combined with fuzzy set theory were applied to assign the weight for each indicator and to define the membership functions. The geographic information system （GIS） was used to manage the spatial database and conduct the spatial analysis. According to the spatial calculation of evaluation model integrated with GIS, the ecological susceptibility of vegetation （crops and forage） was mapped. Three different zones, pastoral area, farming-pastoral ecotones and farming area, were classified by spatial cluster analysis and the maximum likelihood classification for the numeric map of vegetation ecological susceptibility by GIS. This map was validated by the economic statistical result based on the ratio of the output value from animal husbandry in total output value of agriculture by the National Bureau of Statistics in China, indicating that the mapping of the farming-pastoral ecotones may be accepted.展开更多
Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers...Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.展开更多
In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit le...In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit level is carried out.The keystream generated by logistic map is related to the plain-image,which confuses the relationship between the plain-image and the cipher-image.The computer simulation results of statistical analysis,information entropy analysis and sensitivity analysis show that the proposed encryption method is secure and reliable enough to be used for communication application.展开更多
基金supported by the National Western Special Project (Project No. 2003BA901A20)
文摘Conventional farming-pastoral ecotones methods of delineating were not quantitative and could not fully show their spatial distribution. The present paper attempts to develop quantitative methods for mapping farming-pastoral ecotones in China. Nine indicators, related to temperature, precipitation and altitude aspects, were selected to quantify ecological susceptibility of vegetation （crops and forage）. Methods of analytic hierarchy process （AHP） and expert score ranking combined with fuzzy set theory were applied to assign the weight for each indicator and to define the membership functions. The geographic information system （GIS） was used to manage the spatial database and conduct the spatial analysis. According to the spatial calculation of evaluation model integrated with GIS, the ecological susceptibility of vegetation （crops and forage） was mapped. Three different zones, pastoral area, farming-pastoral ecotones and farming area, were classified by spatial cluster analysis and the maximum likelihood classification for the numeric map of vegetation ecological susceptibility by GIS. This map was validated by the economic statistical result based on the ratio of the output value from animal husbandry in total output value of agriculture by the National Bureau of Statistics in China, indicating that the mapping of the farming-pastoral ecotones may be accepted.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(12CX06071A)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.
文摘In this paper,a novel bit-level image encryption method based on dynamic grouping is proposed.In the proposed method,the plain-image is divided into several groups randomly,then permutation-diffusion process on bit level is carried out.The keystream generated by logistic map is related to the plain-image,which confuses the relationship between the plain-image and the cipher-image.The computer simulation results of statistical analysis,information entropy analysis and sensitivity analysis show that the proposed encryption method is secure and reliable enough to be used for communication application.
