基于有向图建模的航天器自动化测试研究与实现

为了提高航天器测试效率及测试质量,研究了在无法给出航天器数学模型的情况下利用知识定义方法实现自动化测试的原理。通过对航天器静态测试和动态测试任务的分析,建立了航天器测试任务中测试业务的数学表示模型。基于有向图理论,建立了航天器测试过程的基本模型,提出了一种形式简单、易于理解、便于应用的表格方式航天器自动化测试语言(SATL),给出了SATL应用示例,开发了航天器自动化测试软件(SATS)。SATL和SATS成功应用于"神舟"飞船及多颗卫星各阶段测试,实际应用表明了该方案具有较强的工程实用性。

有机物同分异构体生成算法及其实现

介绍一种自动生成有机物同分异构体的算法 ,提出了一种可以惟一表示有机物分子结构的表示方法 。

结合C-TTCN和全局行为分析的路由协议一致性测试

以路由协议OSPF(OpenShortestPathFirst)的LSDB(LinkStateDataBase)同步过程为例 ,提出了一种测试架构 ,将C TTCN(ConcurrentTreeandTabularCombinedNo tation)测试模型和全局行为分析器融于一体 ,降低了C

介绍一首记忆名词周延情况的歌诀

A、E、I、O四种类型直言判断的名词周延性问题是很重要的。它在直言三段论的推理中,有着更加突出的作用。学习逻辑的人,不仅要理解它,而且要记住它,运用它。在逻辑书上,一般采用两种形式来表示它。 一种是用语言表述: 全称判断主词周延。 特称判断主词不周延。 肯定判断宾词不周延。 否定判断宾词周延。 另一种是列表表示:

致大会贺词

尊敬的各位来宾、各位代表: 值此广西钱币学会第四届会员代表大会召开之际,我代表人民银行南宁中心支行,向广西钱币学会,向与会代表表示衷心的祝贺。

Tri-party deep network representation learning using inductive matrix completion

Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.

并发TTCN测试执行器的实现研究

并发 TTCN同时拥有多个测试部件的特性 ,改变了整个测试框架 ,也带来了如何协调和管理测试部件之间通信的新问题 .本文在传统 TTCN测试执行模块的基础上为并发 TTCN设计了测试执行模块的整体框架 ,并进行了具体的实现 .

Coset Structure of Spin Group

This article considers One example is also given to take a the coset structure closer look at what of spin group via analyzing the expression of its representation. the coset and the subgroup are.

A 9×9 Matrix Representation of Birman-Wenzl-Murakami Algebra and Berry Phase in Yang-Baxter System

We present a 9×9 S-matrix and E-matrix.A representation of specialized Birman-Wenzl-Murakami algebra is obtained.Starting from the given braid group representation S-matrix,we obtain the trigonometric solution of Yang-Baxter equation.A unitary matrix R(x,φ1,φ2)is generated via the Yang-Baxterization approach.Then we construct a Yang-Baxter Hamiltonian through the unitary matrix R(x,φ1,φ2).Berry phase of this Yang-Baxter system is investigated in detail.

Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network

Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

New Formulation of Weyl Quantization Scheme in Coherent State Representation

By virtue of the technique of integration within an ordered product of operators we present a new formulation of the Weyl quantization scheme in the coherent state representation, which not only brings convenience for calculating the Weyl correspondence of normally ordered operators, but also directly leads us to find both the coherent state representation and the Weyl ordering representation of the Wigner operator.

A new discriminative sparse parameter classifier with iterative removal for face recognition

Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.

Multi-domain Boundary Element Method with Dissipation

The wave diffraction and radiation around a floating body is considered within the framework of the linear potential theory in a fairly perfect fluid. The fluid domain extended infinitely in the horizontal directions but is limited by the sea bed, the body hull, and the part of the free surface excluding the body waterplane, and is subdivided into two subdomains according to the body geometry. The two subdomains are connected by a control surface in fluid. In each subdomain, the velocity potential is described by using the usual boundary integral representation involving Green functions. The boundary integral equations are then established by satisfying the boundary conditions and the continuous condition of the potential and the normal derivation across the control surface. This multi-domain boundary element method （MDBEM） is particularly interesting for bodies with a hull form including moonpools to which the usual BEM presents singularities and slow convergence of numerical results. The application of the MDBEM to study the resonant motion of a water column in moonpools shows that the MDBEM provides an efficient and reliable prediction method.

与外宾共赏谜

与外宾共赏谜一次，一个外国代表团来华访问，去杭州参观。宾主同游西湖，正值春光明媚，一路谈笑风生。忽然，一位外国代表表示对中国的猜谜活动很感兴趣，并出一谜让主人猜：你一只，我一只，一家屋里有几只；全国只有十几只，你说稀奇不稀奇？我陪同代表听罢笑道：“这...

s-ordering operator expansions of quantum-mechanical fundamental representations and their applications

We show that the quantum-mechanical fundamental representations, say, the coordinate representation, the coherent state representation, the Fan-Klauder entangled state representation can be recast into s-ordering operator expansion, which is elegant in form and has many applications in deriving new operator identities. This demonstrates that Dirac's symbolic method can be merged into Newton-Leibniz integration theory in a broad way.

On tempered and square integrable representations of classical p-adic groups

This paper has two aims. The first is to give a description of irreducible tempered representations of classical p-adic groups which follows naturMly the classification of irreducible square integrable representations modulo cuspidal data obtained by Mceglin and the author of this article （2002）. The second aim of the paper is to give a description of an invariant （partially defined function） of irreducible square integrable representation of a classical p-adic group （defined by Mceglin using embeddings） in terms of subquotients of Jacquet modules. As an application, we describe behavior of partially defined function in one construction of square integrable representations of a bigger group from such representations of a smaller group （which is related to deformation of Jordan blocks of representations）.

Quantum Fisher Information for Density Matrices with Arbitrary Ranks

We provide a new expression of the quantum Fisher information(QFI) for a general system.Utilizing this expression,the QFI for a non-full rank density matrix is only determined by its support.This expression can bring convenience for an infinite-dimensional density matrix with a finite support.Besides,a matrix representation of the QFI is also given.