With a projective equation and a linear variable separation method, this paper derives new families of variable separation solutions （including solitory wave solutions, periodic wave solutions, and rational function ...With a projective equation and a linear variable separation method, this paper derives new families of variable separation solutions （including solitory wave solutions, periodic wave solutions, and rational function solutions） with arbitrary functions for （2＋1）-dimensional generalized Breor-Kaup （GBK） system. Based on the derived solitary wave excitation, it obtains fusion and fission solitons.展开更多
The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coo...The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coordination is also discussed. First, based on the analysis of traditi onal project management, project management and coordination of distributed coop erative product design in the virtual enterprise is put forward. Then, aiming at the characteristics of a distributed concurrent product design process, the inh erent rules and complex interrelations in product development are studied. Accor dingly, the architecture of project management of distributed cooperative produc t design in a virtual enterprise is presented to adapt to distributed concurrent development of complex products. The main advantages of the architecture are al so discussed. Finally, the emphasis is placed on the project management process. Its main functions are set forth, such as project definition, task decompositio n and distribution, resource constraints and dynamic resource scheduling, proces s fusion, task scheduling and monitoring, project plan, cost and quality evaluat ion, etc.展开更多
随着数据来源方式的多样化发展,多视图聚类成为研究热点。大多数算法过于专注利用图结构寻求一致表示,却忽视了如何学习图结构本身;此外,一些方法通常基于固定视图进行算法优化。为了解决这些问题,提出了一种基于相似图投影学习的多视...随着数据来源方式的多样化发展,多视图聚类成为研究热点。大多数算法过于专注利用图结构寻求一致表示,却忽视了如何学习图结构本身;此外,一些方法通常基于固定视图进行算法优化。为了解决这些问题,提出了一种基于相似图投影学习的多视图聚类算法(multi-view clustering based on similarity graph projection learning, MCSGP),通过利用投影图有效地融合了全局结构信息和局部潜在信息到一个共识图中,而不仅是追求每个视图与共识图的一致性。通过在共识图矩阵的图拉普拉斯矩阵上施加秩约束,该算法能够自然地将数据点划分到所需数量的簇中。在两个人工数据集和七个真实数据集的实验中,MCSGP算法在人工数据集上的聚类效果表现出色,同时在涉及21个指标的真实数据集中,有17个指标达到了最优水平,从而充分证明了该算法的优越性能。展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
基金supported by the Natural Science Foundation of Zhejiang Province of China (Grant Nos.Y604106 and Y606252)the Natural Science Foundation of Zhejiang Lishui University of China (Grant No.KZ09005)
文摘With a projective equation and a linear variable separation method, this paper derives new families of variable separation solutions （including solitory wave solutions, periodic wave solutions, and rational function solutions） with arbitrary functions for （2＋1）-dimensional generalized Breor-Kaup （GBK） system. Based on the derived solitary wave excitation, it obtains fusion and fission solitons.
文摘The architecture of project management of distributed concurrent product design in a virtual enterprise is put forward. T he process of project management and its functions are presented. Product design process coordination is also discussed. First, based on the analysis of traditi onal project management, project management and coordination of distributed coop erative product design in the virtual enterprise is put forward. Then, aiming at the characteristics of a distributed concurrent product design process, the inh erent rules and complex interrelations in product development are studied. Accor dingly, the architecture of project management of distributed cooperative produc t design in a virtual enterprise is presented to adapt to distributed concurrent development of complex products. The main advantages of the architecture are al so discussed. Finally, the emphasis is placed on the project management process. Its main functions are set forth, such as project definition, task decompositio n and distribution, resource constraints and dynamic resource scheduling, proces s fusion, task scheduling and monitoring, project plan, cost and quality evaluat ion, etc.
文摘随着数据来源方式的多样化发展,多视图聚类成为研究热点。大多数算法过于专注利用图结构寻求一致表示,却忽视了如何学习图结构本身;此外,一些方法通常基于固定视图进行算法优化。为了解决这些问题,提出了一种基于相似图投影学习的多视图聚类算法(multi-view clustering based on similarity graph projection learning, MCSGP),通过利用投影图有效地融合了全局结构信息和局部潜在信息到一个共识图中,而不仅是追求每个视图与共识图的一致性。通过在共识图矩阵的图拉普拉斯矩阵上施加秩约束,该算法能够自然地将数据点划分到所需数量的簇中。在两个人工数据集和七个真实数据集的实验中,MCSGP算法在人工数据集上的聚类效果表现出色,同时在涉及21个指标的真实数据集中,有17个指标达到了最优水平,从而充分证明了该算法的优越性能。
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
