Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP...Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.展开更多
Regional design is the design method based on the geographical characteristics, and the natural environment is basic factors. This paper puts forward to the regional product design thought patterns and makes some brie...Regional design is the design method based on the geographical characteristics, and the natural environment is basic factors. This paper puts forward to the regional product design thought patterns and makes some brief comments on its value and social significance of the design, in a long run, with exploratory guiding significance.展开更多
To realize real-time monitoring and short-term forecasting and forewarning of coalmine ventilation systems(CVS), in this paper, we first established a joint surface and underground CVS safety management system consist...To realize real-time monitoring and short-term forecasting and forewarning of coalmine ventilation systems(CVS), in this paper, we first established a joint surface and underground CVS safety management system consisting of main ventilation fan, safety-partition linked passageways, and air-required locations. We then applied chaos theory to identify the air quantity and gas concentration of underground partition boundaries, and adopted a fixed data quantity, multi-step progressive, weighted first-order local-domain method to setup a chaos prediction model and a CVS safety forecasting and forewarning system formed by the normal change level, orange forewarning level, and red alarm level. We next conduct the on-field application of the system in a coalmine in Jining, Shandong, China. The results showed that (1) in the statistical scale of 5 min, the changes in both air quantity and gas concentration along CVS partition airflow boundaries were characteristic of chaos and could be used for short-term chaos prediction, and the latter was more chaotic than the former;(2) the setup chaos prediction model had a higher prediction precision and the established safety prediction system could not only predict the variation in CVS stability but also reflect the rationality of underground mining intensity. Thus, this CVS safety forecasting and forewarning system is of better application value.展开更多
In overcoming the drawbacks of traditional interval perturbation method due to the unpredictable effect of ignoring higher order terms,a modified parameter perturbation method is presented to predict the eigenvalue in...In overcoming the drawbacks of traditional interval perturbation method due to the unpredictable effect of ignoring higher order terms,a modified parameter perturbation method is presented to predict the eigenvalue intervals of the uncertain structures with interval parameters.In the proposed method,interval variables are used to quantitatively describe all the uncertain parameters.Different order perturbations in both eigenvalues and eigenvectors are fully considered.By retaining higher order terms,the original dynamic eigenvalue equations are transformed into interval linear equations based on the orthogonality and regularization conditions of eigenvectors.The eigenvalue ranges and corresponding eigenvectors can be approximately predicted by the parameter combinatorial approach.Compared with the Monte Carlo method,two numerical examples are given to demonstrate the accuracy and efficiency of the proposed algorithm to solve both the real eigenvalue problem and complex eigenvalue problem.展开更多
In a case study in Tao River Basin, China, we derived a high spatial-resolution regional distribution of evapotranspiration(ET) using the single crop coefficient method and Budyko equation. We then further analyzed th...In a case study in Tao River Basin, China, we derived a high spatial-resolution regional distribution of evapotranspiration(ET) using the single crop coefficient method and Budyko equation. We then further analyzed the spatio-temporal characteristics of this diverse eco-hydrological basin from 2001–2010. The results suggest that the single crop coefficient method based on leaf area index captures better spatial and temporal dynamics of the regional ET than did the Budyko Equation method. The rising temperature was the main reason for the increasing ET in the Tao River Basin during 2001–2010. Areas with high ET efficiency were distributed mainly in the areas where the vegetation coverage was high, and a lower runoff coefficient responded. The estimated spatial patterns of ET allowed an improved understanding of the eco-hydrological processes within the Tao River Basin and the method used might be generalized as a reference for future regional-scale eco-hydrological research.展开更多
基金Projects(60903082,60975042)supported by the National Natural Science Foundation of ChinaProject(20070217043)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.
文摘Regional design is the design method based on the geographical characteristics, and the natural environment is basic factors. This paper puts forward to the regional product design thought patterns and makes some brief comments on its value and social significance of the design, in a long run, with exploratory guiding significance.
基金supported by the National Natural Science Foundation of China(Nos.51304128 and 51674158)the Natural Science Foundation of Shandong Province(No.ZR2013EEQ015)
文摘To realize real-time monitoring and short-term forecasting and forewarning of coalmine ventilation systems(CVS), in this paper, we first established a joint surface and underground CVS safety management system consisting of main ventilation fan, safety-partition linked passageways, and air-required locations. We then applied chaos theory to identify the air quantity and gas concentration of underground partition boundaries, and adopted a fixed data quantity, multi-step progressive, weighted first-order local-domain method to setup a chaos prediction model and a CVS safety forecasting and forewarning system formed by the normal change level, orange forewarning level, and red alarm level. We next conduct the on-field application of the system in a coalmine in Jining, Shandong, China. The results showed that (1) in the statistical scale of 5 min, the changes in both air quantity and gas concentration along CVS partition airflow boundaries were characteristic of chaos and could be used for short-term chaos prediction, and the latter was more chaotic than the former;(2) the setup chaos prediction model had a higher prediction precision and the established safety prediction system could not only predict the variation in CVS stability but also reflect the rationality of underground mining intensity. Thus, this CVS safety forecasting and forewarning system is of better application value.
基金supported by the National Natural Science Foundation of China(Grant No.90816024)Defense Industrial Technology Development Program(Grant Nos.A2120110001 and B2120110011)111 Project(Grant No.B07009)
文摘In overcoming the drawbacks of traditional interval perturbation method due to the unpredictable effect of ignoring higher order terms,a modified parameter perturbation method is presented to predict the eigenvalue intervals of the uncertain structures with interval parameters.In the proposed method,interval variables are used to quantitatively describe all the uncertain parameters.Different order perturbations in both eigenvalues and eigenvectors are fully considered.By retaining higher order terms,the original dynamic eigenvalue equations are transformed into interval linear equations based on the orthogonality and regularization conditions of eigenvectors.The eigenvalue ranges and corresponding eigenvectors can be approximately predicted by the parameter combinatorial approach.Compared with the Monte Carlo method,two numerical examples are given to demonstrate the accuracy and efficiency of the proposed algorithm to solve both the real eigenvalue problem and complex eigenvalue problem.
基金supported by the Doctoral Program of China’s Higher Education Research Fund(Grant No.20110211110011)the National Natural Science Foundation of China(Grant Nos.41001014,41240002,51209119)
文摘In a case study in Tao River Basin, China, we derived a high spatial-resolution regional distribution of evapotranspiration(ET) using the single crop coefficient method and Budyko equation. We then further analyzed the spatio-temporal characteristics of this diverse eco-hydrological basin from 2001–2010. The results suggest that the single crop coefficient method based on leaf area index captures better spatial and temporal dynamics of the regional ET than did the Budyko Equation method. The rising temperature was the main reason for the increasing ET in the Tao River Basin during 2001–2010. Areas with high ET efficiency were distributed mainly in the areas where the vegetation coverage was high, and a lower runoff coefficient responded. The estimated spatial patterns of ET allowed an improved understanding of the eco-hydrological processes within the Tao River Basin and the method used might be generalized as a reference for future regional-scale eco-hydrological research.
