The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d...The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.展开更多
μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is propos...μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.展开更多
A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means ...A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.展开更多
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional...A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.展开更多
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c...A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.展开更多
探究施钙对不同花生荚果发育时期光合碳在植株-土壤系统分配的影响,有利于改善钙肥管理,提升花生产量和土壤有机碳含量。本研究选用普通大花生品种‘花育22’,设置CaO 0、75、150和300 kg hm^(-2)4个施钙梯度,分别记为T0、T1、T2、T3,...探究施钙对不同花生荚果发育时期光合碳在植株-土壤系统分配的影响,有利于改善钙肥管理,提升花生产量和土壤有机碳含量。本研究选用普通大花生品种‘花育22’,设置CaO 0、75、150和300 kg hm^(-2)4个施钙梯度,分别记为T0、T1、T2、T3,于盆栽条件下研究施钙量对花生产量和不同荚果发育时期光合碳在花生植株-土壤系统中分配的影响。结果表明,不同施钙量对花生植株总干物质积累无明显影响。适宜施钙量可显著降低花生千克果数和千克仁数,提升花生出仁率、饱果率和荚果产量,在2018年和2019年,T2处理荚果产量较T0可分别提升17.5%和25.1%。基于施钙量与花生荚果和籽仁产量的拟合分析发现,当钙肥施用量为165 kg hm^(-2)和173 kg hm^(-2)时,可分别获得最高的花生荚果和籽仁产量。适宜施钙量可明显提升鸡咀幼果期和荚果膨大期花生植株光合^(13)C的积累量,提升各荚果发育时期^(13)C在花生籽仁中的分配比例,其中,在荚果定型期和籽仁充实期,T2和T3处理下^(13)C在花生籽仁中的分配比例分别可达33.4%~37.2%和38.7%~40.0%。适宜施钙量还可提高花生植株光合^(13)C在土壤中的分配比例,最高可达52.6%(T2),但随着花生荚果发育进程的推进,此分配比例逐渐降低。综上,适宜施钙量可调控不同花生荚果发育时期光合^(13)C在植株-土壤系统的分配,显著提升花生产量和光合^(13)C在花生籽仁和土壤中的分配比例;本研究条件下,推荐适宜施钙量(CaO)为173 kg hm^(-2)。展开更多
基金supported by National Natural Science Foundation of China(61403244,61304031)Key Project of Science and Technology Commission of Shanghai Municipality(14JC1402200)+3 种基金the Shanghai Municipal Commission of Economy and Informatization under Shanghai Industry-University-Research Collaboration(CXY-2013-71)the Science and Technology Commission of Shanghai Municipality under’Yangfan Program’(14YF1408600)National Key Scientific Instrument and Equipment Development Project(2012YQ15008703)Innovation Program of Shanghai Municipal Education Commission(14YZ007)
文摘The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
基金Supported by the "Chunhui" Plan of Ministry of Education of China (Z2005-2-11013)
文摘μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.
基金Project supported by the 15th Plan for National Defence Preventive Research Project (Grant No.413030201)
文摘A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.
文摘A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
基金supported by the National Natural Science Foundation of China(51675253)
文摘A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.
文摘探究施钙对不同花生荚果发育时期光合碳在植株-土壤系统分配的影响,有利于改善钙肥管理,提升花生产量和土壤有机碳含量。本研究选用普通大花生品种‘花育22’,设置CaO 0、75、150和300 kg hm^(-2)4个施钙梯度,分别记为T0、T1、T2、T3,于盆栽条件下研究施钙量对花生产量和不同荚果发育时期光合碳在花生植株-土壤系统中分配的影响。结果表明,不同施钙量对花生植株总干物质积累无明显影响。适宜施钙量可显著降低花生千克果数和千克仁数,提升花生出仁率、饱果率和荚果产量,在2018年和2019年,T2处理荚果产量较T0可分别提升17.5%和25.1%。基于施钙量与花生荚果和籽仁产量的拟合分析发现,当钙肥施用量为165 kg hm^(-2)和173 kg hm^(-2)时,可分别获得最高的花生荚果和籽仁产量。适宜施钙量可明显提升鸡咀幼果期和荚果膨大期花生植株光合^(13)C的积累量,提升各荚果发育时期^(13)C在花生籽仁中的分配比例,其中,在荚果定型期和籽仁充实期,T2和T3处理下^(13)C在花生籽仁中的分配比例分别可达33.4%~37.2%和38.7%~40.0%。适宜施钙量还可提高花生植株光合^(13)C在土壤中的分配比例,最高可达52.6%(T2),但随着花生荚果发育进程的推进,此分配比例逐渐降低。综上,适宜施钙量可调控不同花生荚果发育时期光合^(13)C在植株-土壤系统的分配,显著提升花生产量和光合^(13)C在花生籽仁和土壤中的分配比例;本研究条件下,推荐适宜施钙量(CaO)为173 kg hm^(-2)。
