大型电机二维温度场的网络拓扑法计算

从传热学的基本理论出发,通过离散积分形式的热传导方程,构造出了网络拓扑法的通用计算格式。采用网络拓扑法对具有强迫通风系统的电机温度场进行了分析和计算,得出的计算结果说明网络拓扑法是一种简单易行、具有较高精确度的计算方法。

用网络拓扑法计算同步发电机定子的温度分布

应用网络拓扑法求解了轴向通风同步发电机定子的三维温度分布。对发电机定子散热的边界条件作了妥善处理。额定工况下的计算值与实测值相比较为接近。

用网络拓扑法计算同步发电机定子温度场

应用网络拓扑法对轴向通风同步发电机定子温度场进行了分析和计算。对发电机定子散热的边界条件进行了妥善的处理。在额定工况下的计算值与实测值基本相符。

用网络拓扑法计算单相串励电动机电枢温度场

采用网络拓扑法，求解单相串励电动机电枢三维稳态温度场分布。对电枢绕组的等效导热系数作了具体的分析计算；对电动机电枢散热的边界条件进行了妥善的处理。最后得到一个适合于计算单相串励电动机电枢温度场的计算程序，并对１０５０Ｗ单相串励电动机（平均）温升试验数据进行了验证，计算值与实测值基本吻合。

对偶网络拓扑法在场问题计算中的应用

网络拓扑法已被实践证明为一种有效实用的数值计算方法。它以数学分析简单、物理概念明确、单元剖分灵活、适用性强等优点,已被越来越多的场计算专家所采用,成为一种较优秀的数值分析方法。本文在普通网络拓扑法的基础上,以温度场计算为例,通过建立其能量泛函并求其极值,导出一种高精度的网络拓扑法——对偶网络拓扑法。在给定的求解精度条件下,由于两组网络计算时产生误差之间的补偿作用,使用该方法能有效地减少计算存贮空间、缩短计算用时。这对用微机求解复杂场问题是很有意义的。本文最后的实例虽然是针对温度场分析的,但对其他场问题完全适用,故具有普遍性。

电机电磁场、温度场数值计算的对偶网络拓扑法

分析了一种高精度的场计算方法——对偶网络拓扑法,它能有效地减少计算存贮空间,缩短计算时间,提高求解精度。

3-D SPIDERGON:3-D TOPOLOGY OF DELAY OPTIMIZATION FOR NETWORKS-ON-CHIP

A 3-D topology architeeture based on Spidergon and its generation method are proposed. Aiming at establishing relationships between the topology architecture and the latency, the 3-D topology latency model based on prototype is proposed, and then the optimization topology structure with minimum latency is determined based on it. Meanwhile, in accordance with the structure, the adaptive routing algorithm is designed. The algorithm sets longitudinal direction priority to adaptively searching the equivalent minimum path between the source nodes and the destination nodes in order to increase network throughput. Simulation shows that in case of approximate saturation network, compared with the same scale 3-D mesh structure, 3-D Spidergon has 17% less latency and 16.7% more network throughput.

圆筒型直线感应电动机温度场的计算

从直线感应电动机发热的物理概念出发,引入有限元法和网络拓扑法两种计算模型。比较两种方法在圆筒型直线感应电动机温度场计算中的优劣。

A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem

The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.

Survivable Virtual Network Mapping Using Optimal Backup Topology in Virtualized SDN

Software-Defined Network architecture offers network virtualization through a hypervisor plane to share the same physical substrate among multiple virtual networks. However, for this hypervisor plane, how to map a virtual network to the physical substrate while guaranteeing the survivability in the event of failures, is extremely important. In this paper, we present an efficient virtual network mapping approach using optimal backup topology to survive a single link failure with less resource consumption. Firstly, according to whether the path splitting is supported by virtual networks, we propose the OBT-I and OBT-II algorithms respectively to generate an optimal backup topology which minimizes the total amount of bandwidth constraints. Secondly, we propose a Virtual Network Mapping algorithm with coordinated Primary and Backup Topology （VNM-PBT） to make the best of the substrate network resource. The simulation experiments show that our proposed approach can reduce the average resource consumption and execution time cost, while improving the request acceptance ratio of VNs.

Simultaneous Optimization of Synthesis and Scheduling of Cleaning in Flexible Heat Exchanger Networks

A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network(HEN)by genetic/simulated annealing algorithms(GA/SA).Through taking into account the effect of fouling process on optimal network topology,a preliminary network structure possessing two-fold oversynthesis is obtained by means of pseudo-temperature enthalpy(T-H)diagram approach prior to simultaneous optimization.Thus,the computational complexity of this problem classified as NP(Non-deterministic Polynomial)-complete can be significantly reduced.The promising matches resulting from preliminary synthesis stage are further optimized in parallel with their heat exchange areas and cleaning schedule.In addition,a novel continu- ous time representation is introduced to subdivide the given time horizon into several variable-size intervals according to operating periods of heat exchangers,and then flexible HEN synthesis can be implemented in dynamic manner.A numerical example is provided to demonstrate that the presented strategy is feasible to decrease the total annual cost(TAC)and further improve network flexibility,but even more important,it may be applied to solve large-scale flexible HEN synthesis problems.

PTCP Incast in Data Center Networks

In recent years,dual-homed topologies have appeared in data centers in order to offer higher aggregate bandwidth by using multiple paths simultaneously.Multipath TCP(MPTCP) has been proposed as a replacement for TCP in those topologies as it can efficiently offer improved throughput and better fairness.However,we have found that MPTCP has a problem in terms of incast collapse where the receiver suffers a drastic goodput drop when it simultaneously requests data over multiple servers.In this paper,we investigate why the goodput collapses even if MPTCP is able to actively relieve hot spots.In order to address the problem,we propose an equally-weighted congestion control algorithm for MPTCP,namely EW-MPTCP,without need for centralized control,additional infrastructure and a hardware upgrade.In our scheme,in addition to the coupled congestion control performed on each subflow of an MPTCP connection,we allow each subflow to perform an additional congestion control operation by weighting the congestion window in reverse proportion to the number of servers.The goal is to mitigate incast collapse by allowing multiple MPTCP subflows to compete fairly with a single-TCP flow at the shared bottleneck.The simulation results show that our solution mitigates the incast problem and noticeably improves goodput in data centers.

Fuzzy adaptive learning control network with sigmoid membership function

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

A distributed routing algorithm based-on simplified topology in LEO satellite networks

In this paper, a distributed muting strategy based on simplified topology （DRBST） was proposed for LEO satellite networks. The topology of LEO satellite networks was simplified aiming at minimizing intersatellite links handover number. To optimize the route based on the simplified topology, we considered not only the transmission delay but also the queuing delay and the processing delay, which were analyzed using Markov chain and determined using a novel methodology. The DRBST algorithm was simulated in a LEO satellite networks model built using OPNET. The simulation results demonstrate that the low complexity DRBST algorithm can guarantee end-to-end delay bound. Moreover, the muting protocol cost is much less than traditional algorithms.

Calculation of Minimal Dominating Set in Wireless Sensor Network with Host Switch-on/off

This paper proposes a simple and efficient distributed algorithm for calculating minimal dominating set in wireless sensor network. This method can avoid maintaining the connectivities between backbone hosts. Considering that the hosts in mobile networks have different characteristics, this paper proposes a method of calculating minimal dominating set with weight. The nodes can be chosen to form a minimal dominating set when the network topology changes. For the host switch on/off operation, the updating algorithm was provided. The change in the status of a hostaffects only the status of hosts in the restricted vicinity. Simulation results show that the proposed method can ensure fewer dominators but with higher weight to form the minimal dominating set and the nodes can be adaptive to the changes of network topology.

Prediction of Temperature Daily Profile by Stochastic Update of Backpropagation through Time Algorithm

The authors will examine prediction of temperature daily profile using various modifications of BPTT （backpropagation through time algorithm） done by stochastic update in the artificial RCNN （recurrent neural networks）. The general introduction was provided by Salvetti and Wilamowski in 1994 in order to improve probability of convergence and speed of convergence. This update method has also one another quality, its implementation is simple for arbitrary network topology. In stochastic update scenario, constant number of weights/neurons is randomly selected and updated. This is in contrast to classical ordered update, where always all weights/neurons are updated. Stochastic update is suitable to replace classical ordered update without any penalty on implementation complexity and with good chance without penalty on quality of convergence. They have provided first experiments with stochastic modification on BP （backpropagation algorithm） used for artificial FFNN （feed-forward neural network） in detail described in the article ＂Stochastic Weight Update in the Backpropagation Algorithm on Feed-Forward Neural Networks＂ presented on the conference IJCNN （International Joint Conference of Neural Networks） 2010 in Barcelona. The BPTT on RCNN uses the history of previous steps stored inside of the NN that can be used for prediction. They will describe exact implementation on the RCNN, and present experiment results on temperature prediction with recurrent neural network topology. The dataset used for temperature prediction consists of the measured temperature from the year 2000 till the end of February 2011. Dataset is split into two groups： training dataset, which is provided to network in learning phase, and testing dataset, which is unknown part of dataset to NN and used to test the ability of NN to predict the temperature and the ability of NN to generalize the model hidden in the temperature profile. The results show promising properties of stochastic weight update with toy-task data, and the higher complexity of the temperature daily profile prediction.

专利引文分析方法研究进展

作为战略情报研究的重要数据来源,专利引文不仅反映技术发展中的知识流动,还可用于技术热点的跟踪以及国家、机构的技术地位识别。首先概述专利引文的定义和来源,根据专利引文类型不同,分别梳理专利-专利引文分析方法和专利-论文引文分析方法的发展脉络及代表性的研究成果,最后总结专利引文研究方法目前存在的问题,并给出应对建议。

ON CONTROL OF STRONG CONSENSUS FOR NETWORKED AGENTS WITH NOISY OBSERVATIONS

The discrete-time first-order multi-agent networks with communication noises are under consideration. Based on the noisy observations, the consensus control is given for networks with both fixed and time-varying topologies. The states of agents in the resulting closed-loop network are updated by a stochastic approximation （SA） algorithm, and the consensus analysis for networks turns to be the convergence analysis for SA. For networks with fixed topologies, the proposed consensus control leads to consensus of agents with probability one if the graph associated with the network is connected. In the case of time-varying topologies, the similar results are derived if the graph is jointly connected in a fixed time period. Compared with existing results, the networks considered here are in a more general setting under weaker assumptions and the strong consensus is established by a simpler proof.

Global Systemic Financial Risk Spillovers and Their External Shocks

The shock of the global financial crisis sparked widespread concern across the world about systemic financial risk and led to the reexamination of regulatory mechanisms.The traditional principle of“too big to fail”underwent a transformation into the new idea of“too interconnected to fail.”We used Directed Acyclic Graph(DAG)technology and network topology analysis to examine the dynamic evolution of global systemic financial risk and the risk trends in global financial markets from the perspective of network connectivity.Our findings show that financial markets in the Chinese Mainland are net receivers of risk spillovers and that systemic financial risk has a clear cross-market contagion effect due to a global volatility spillover scale of 64 percent.To maintain the stability and security of China’s financial markets,consideration should be given to the regulatory precept of“too interconnected to fail”in establishing macro-prudential risk prevention mechanisms.

Synchronization Research of a Modified Time-Delayed Multi-agent Network Model

The synchronization of time-delayed multi-agent networks with connected and directed topology is studied. Based on the correlative work about the agent synchronization, a modified model is presented, in which each communication receiver is distributed a delay 7. In addition, a proportional term k is introduced to modulate the delay range and to guarantee the synchronization of each agent. Two new parameters mentioned above are only correlative to the network topology, and a theorem about their connections is derived by both frequency domain method and geometric method. Finally, the theoretical result is illustrated by numerical simulations.