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| 1 | From knowledge based software engineering to knowware based software engineering显示文摘The first part of this paper reviews our efforts on knowledge-based software engi- neering, namely PROMIS, started from 1990s. The key point of PROMIS is to gen- erate applications automatically based on domain knowledge as well as software knowledge. That is featured by separating the development of domain knowledge from the development of software. But in PROMIS, we did not find an appropriate representation for the domain knowledge. Fortunately, in our recent work, we found such a carrier for knowledge modules, i.e. knowware. Knowware is a commercial- ized form of domain knowledge. This paper briefly introduces the basic definitions of knowware, knowledge middleware and knowware engineering. Three life circle models of knowware engineering and the design of corresponding knowware im- plementations are given. Finally we discuss application system automatic genera- tion and domain knowledge modeling on the J2EE platform, which combines the techniques of PROMIS, knowware and J2EE, and the development and deployment framework, i.e. PROMIS/KW**. | LU RuQian JIN Zhi | 2008 | Science in China(Series F)2008，51，6： | 3 |
| 2 | Locality preserving projection on SPD matrix Lie group: algorithm and analysis显示文摘Symmetric positive definite(SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data is impossible. The SPD matrix must first be transformed into a long vector, and then the dimension of this vector must be reduced. However, this approach breaks the spatial structure of the SPD matrix space. To overcome this limitation, we propose a new dimension reduction algorithm on SPD matrix space to transform high-dimensional SPD matrices into low-dimensional SPD matrices. Our work is based on the fact that the set of all SPD matrices with the same size has a Lie group structure, and we aim to transform the manifold learning to the SPD matrix Lie group. We use the basic idea of the manifold learning algorithm called locality preserving projection(LPP)to construct the corresponding Laplacian matrix on the SPD matrix Lie group. Thus, we call our approach Lie-LPP to emphasize its Lie group character. We present a detailed algorithm analysis and show through experiments that Lie-LPP achieves effective results on human action recognition and human face recognition. | Yangyang LI Ruqian LU | 2018 | Science China(Information Sciences)2018，61，9： | 3 |
| 3 | P/R nets and P/R processes( Ⅰ )显示文摘 | Lu Ruqian | 1992 | Science In China(Series E)1992，35，1： | 1 |
| 4 | P/R nets and P/R processes( Ⅱ ) 显示文摘 | Lu Ruqian | 1992 | Science In China(Series E)1992，35，1： | 1 |
| 5 | From hardware to software to knowware: IT's third liberation显示文摘 | Lu Ruqian | 2005 | IEEE Intelligent Systems2005，20，2： | 1 |
| 6 | Formal Ontology:Foundation of Domain Knowledge Sharing and Reusing显示文摘 | Lu Ruqian Jin Zhi | 2002 | J comput Sci & Technol2002，17，5： | 1 |
| 7 | P/R nets and P/R processes(Ⅱ) 显示文摘 | | 1992 | Science In China (Series E)1992，35，1： | 1 |
| 8 | Applying Ricci flow to high dimensional manifold learning显示文摘In machine learning, a high dimensional data set such as the digital image of a human face is often viewed as a point set distributed on a differentiable manifold. In many cases, the intrinsic dimension of this manifold is low but the representation dimension of the data points is high. To ease data processing requirements, manifold learning(ML) techniques can be used to reduce a high dimensional manifold(HDM)to a low dimensional one while keeping the essential geometric properties, such as relative distances between points, unchanged. Traditional ML algorithms often assume that the local neighborhood of any point on an HDM is roughly equal to the tangent space at that point. This assumption leads to the disadvantage that the neighborhoods of points on the manifold, though they have a very different curvature, will be treated equally and will be projected to a lower dimensional space. The curvature is a different way of manifold processing, where traditional dimension reduction is ineffective at preserving the neighborhood.To overcome this obstacle, we perform an 'operation' on the HDM using Ricci flow before a manifold’s dimension reduction. More precisely, with the Ricci flow, we transform each local neighborhood of the HDM to a constant curvature patch. The HDM, as a whole, is then transformed into a subset of a sphere with constant positive curvature. We compare the proposed algorithm with other traditional manifold learning algorithms. Experimental results have shown that the proposed method outperforms other ML algorithms with a better neighborhood preserving rate. | Yangyang LI Ruqian LU | 2019 | Science China(Information Sciences)2019，62，9： | 1 |
| 9 | Networked Knowledge and Complex Networks:An Engineering View显示文摘Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed. | Jinhu Lü Guanghui Wen Ruqian Lu Yong Wang Songmao Zhang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，8： | 1 |
| 10 | Formal Ontology:Foundation of Domain Knowledge Sharing and Resuing显示文摘 | Lu Ruqian Jin Zhi | 2002 | J Comput Sci & Technol2002，17，5： | 1 |
| 11 | Temperature-Dependent Photoconductance of Heavily Doped ZnO Nanowires显示文摘做 Ga 的 ZnO nanowires 被一个搏动的激光化学药品蒸汽免职方法综合了。水晶结构和光致发光系列显示掺杂物原子很好集成于 ZnO wurtzite 格子。在不同温度的光电流性质系统地为作为一台三终端的设备设置的 nanowires 被调查了。在试验性的热点之中,显著 semiconductor-to-metal 转变发生在紫外 band-to-band 刺激之上。这是在电子活动性从急速地提高的库仑相互作用并且表面散布产生的减小的后果。另一个特征是在在轻照耀之上的 220 和 320 K 的二条抵抗山谷的可再现的存在。这现象从在从本国的缺点以及外来的 Ga 掺杂物产生的杂质乐队套住和 detrapping 过程发源。这个工作在 quasi-one-dimensional 结构,提高的库仑相互作用,散布的表面,和状态能显著地影响的杂质由于维的监禁表明那费用运输。 | Dongdong Li Liang Zhao Ruqian Wu Carsten Ronning G. Lu | 2011 | Nano Research2011，4，11： | 0 |
| 12 | A direct product decomposition of QMV algebras显示文摘We study the direct product decomposition of quantum many-valued algebras (QMV algebras) which generalizes the decomposition theorem of ortholattices (orthomodular lattices).In detail,for an idempo- tent element of a given QMV algebra,if it commutes with every element of the QMV algebra,it can induce a direct product decomposition of the QMV algebra.At the same time,we introduce the commutant C(S) of a set S in a QMV algebra,and prove that when S consists of idempotent elements,C(S) is a subalgebra of the QMV algebra.This also generalizes the cases of orthomodular lattices. | LU Xian 1 ,SHANG Yun 1,& LU RuQian 1,2 1 Institute of Mathematics,Academy of Mathematics and Systems Science,Beijing 100190,China 2 Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China | 2012 | Science China Mathematics2012，55，4： | 0 |
| 13 | Curvature flow learning:algorithm and analysis显示文摘In order to describe the nonlinear distribution of the image dataset,researchers propose a manifold assumption,called manifold learning(MAL).The geometry information based on a manifold is measured by the Riemannian metric,such as the geodesic distance.Thus,owing to mining the intrinsic geometric structure of the dataset,we need to learn the real Riemannian metric of the embedded manifold.By the Taylor expansion equation of the Riemannian metric,it clearly indicates that the Riemannian metric is relative to the Riemannian curvature.Based on it,we propose a new algorithm to learn the Riemannian metric by adding the curvature information into metric learning.By optimizing the objective function,we obtain a set of iterative equations.We call this model curvature flow.By employing this curvature flow,we obtain a Mahalanobis metric that approaches the Riemannian metric infinitely and can well uncover the intrinsic structure of the embedded manifold.In theory,we analyze several properties of our proposed method,e.g.,the boundedness of the metric and the convergence of curvature flow.To show the effectiveness of our proposed method,we compare our algorithm with several traditional MAL algorithms on three real world datasets.The corresponding results indicate that our proposed method outperforms the other algorithms. | Yangyang LI Ruqian LU | 2022 | Science China(Information Sciences)2022，65，9： | 0 |
| 14 | Geometry Flow-Based Deep Riemannian Metric Learning显示文摘Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness. | Yangyang Li Chaoqun Fei Chuanqing Wang Hongming Shan Ruqian Lu | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，9： | 0 |
| 15 | A peep at knowledge science in a categorical prospect显示文摘 | Ruqian LU | 2016 | Frontiers of Computer Science2016，10，5： | 0 |
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