无刷直流电机转矩脉动抑制方法研究综述

无刷直流电机因寿命长、体积小、输出转矩大和功率密度高等优点,广泛应用于航空航天等精准转矩控制领域,但由于结构、制造工艺和控制方式等原因,无刷直流电机在工作时不可避免会产生转矩脉动问题,限制了其应用范围。针对此问题,分析了转矩脉动产生原因,综述了通过控制电流、控制转矩、增加拓扑结构等方法抑制转矩脉动的研究现状。

旅游度假区公共信息导向系统评价方法的实证研究

针对上海国际旅游度假区的公共信息导向系统评估,提出同时考虑主客观的综合评价方法。根据旅游度假区的特点,建立了同时考虑主观和客观两个方面的公共信息导向系统评价指标体系,接着采用层次分析法和拓扑结构评价法建立综合评价模型。研究表明该方法能较为全面、准确的反映上海国际旅游度假区公共信息导向系统的实际情况,评价结果可以为公共信息导向系统的设计和规划提供参考,从而提高旅游度假区服务水平。

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.

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.

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.

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.

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.

Interactive Protein Data Clustering

In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.

Distribution network planning algorithm based on Hopfield neural network

This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn’t need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.

Algebraic algorithm for reliability index weighted capacity of communication network

Communication network has communication capacity and connection reliability of the links. They canbe independently defined and can be used separately, and when the reliability of a communication network isanalyzed from a macroscopical angle of view, it is more objective to express the performance index of a commu-nication network as a whole. The reliability index weighted capacity is just obtained by integrating these two pa-rameters. It is necessary to further study the algorithm to calculate the reliability index of the communicationnetwork with a complicated topologic structure and a whole algebraic algorithm is therefore proposed for calcula-tion of the reliability index weighted capacity of a communication network with a topologic structure. The wholecomputational procedure of the algorithm is illustrated with a typical example.

Switched Capacitor Network Analysis by Means of TCM

The totally coded method (TCM) reveals the same objective law, which governs the gain calculating for signal flow graph as Mason formula does. This algorithm is carried out merely in the domain of code operation. Based on pure code algorithm, it is more efficient because figure searching is no longer necessary. The code-series ( CS ), which are organized from node association table, have the holoinformation nature, so that both the content and the sign of each gain-term can be determined via the coded method.The principle of this method is obvious and it is suited for computer programming. The capability of the computeraided analysis for Switched Capacitor (SCN) can be enhanced.

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.

Multiobjective Optimization of Truss Topology by Linear/Sequential Linear Programming Method

The present paper deals with a multiobjective optimization of truss topology by either Sequential Linear Programming （SLP） method or Linear Programming （LP） method. The ground structure approach is often used to solve this kind of design problems. In this paper, the topology optimization is formulated as a Multiobjective Optimization Problem （MOP）, which is to find the cross-sectional area of truss members, such that both the total volume of members and the weighted mean compliance are minimized. Based upon the Karush-Kuhn-Tucker conditions （the optimality condition）, the Pareto optimal front of this problem can be obtained theoretically. The truss topology optimization under multiple load cases can be solved by the SLP. On the other hand, the LP such as the Simplex method or the interior point method can be applied to find one of the Pareto optimal solutions of the MOP under single load case. The applications of either the SLP or the LP are illustrated in numerical examples with discussion on characteristics of design results.

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.

Topology optimization of thermoelastic structures using the guide-weight method

The guide-weight method is introduced to solve the topology optimization problems of thermoelastic structures in this paper.First,the solid isotropic microstructure with penalization(SIMP)with different penalty factors is selected as a material interpolation model for the thermal and mechanical fields.The general criteria of the guide-weight method is then presented.Two types of iteration formulas of the guide-weight method are applied to the topology optimization of thermoelastic structures,one of which is to minimize the mean compliance of the structure with material constraint,whereas the other one is to minimize the total weight with displacement constraint.For each type of problem,sensitivity analysis is conducted based on SIMP model.Finally,four classical 2-dimensional numerical examples and a 3-dimensional numerical example considering the thermal field are selected to perform calculation.The factors that affect the optimal topology are discussed,and the performance of the guide-weight method is tested.The results show that the guide-weight method has the advantages of simple iterative formula,fast convergence and relatively clear topology result.

Tomographic TR-PIV measurement of coherent structure spatial topology utilizing an improved quadrant splitting method

In this paper,we calculated the spatial local-averaged velocity strains along the streamwise direction at four spatial scales according to the concept of spatial local-averaged velocity structure function by using the three-dimensional three-component database of time series of velocity vector field in the turbulent boundary layer measured by tomographic time-resolved particle image velocimetry.An improved quadrant splitting method,based on the spatial local-averaged velocity strains together with a new conditional sampling phase average technique,was introduced as a criterion to detect the coherent structure topology.Furthermore,we used them to detect and extract the spatial topologies of fluctuating velocity and fluctuating vorticity whose center is a strong second-quadrant event(Q2) or a fourth-quadrant event(Q4).Results illustrate that a closer similarity of the multi-scale coherent structures is present in the wall-normal direction,compared to the one in the other two directions.The relationship among such topological coherent structures and Reynolds stress bursting events,as well as the fluctuating vorticity was discussed.When other burst events are surveyed(the first-quadrant event Q1 and the third-quadrant event Q3),a fascinating bursting period circularly occurs:Q4-S-Q2-Q3-Q2-Q1-Q4-S-Q2-Q3-Q2-Q1 in the center of such topological structures along the streamwise direction.In addition,the probability of the Q2 bursting event occurrence is slightly higher than that of the Q4 event occurrence.The spatial instable singularity that almost simultaneously appears together with typical Q2 or Q4 events has been observed,which is the main character of the mutual induction mechanism and vortex auto-generation mechanism explaining how the turbulence is produced and maintained.

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.

Compliant mechanisms design based on pairs of curves

The success of compliant mechanism design by structural topology optimization approach depends,to a large extent,on its structural geometry representation scheme.In this work,a novel representation scheme based on pairs of curves is presented.In the representation,the structure is characterized by a set of input/output(I/O) regions.While it is still unknown how the rest of the design space will be occupied by the structure,the I/O regions must exist somewhere because any structure must have parts which interact with its surroundings by way of at least one loading region,one support region,and one output region.For a valid structural design,pairs of Bezier curves are used to connect I/O regions in order to form one single connected load-bearing structure.The boundary is explicitly described,so the need for smoothening of the blurred and jagged edges can be avoided by developing such a representation scheme to directly generate smooth boundary structures.With the scheme,shape and topology can be optimized simultaneously,and the obtained topology solutions have no check-board phenomena nor intermediate zones.A multi-objective genetic algorithm is then applied to couple with the representation scheme for defining and encoding the structural geometry in the form of graph.The solution framework is integrated with a nonlinear fixed grid finite element method(FG-FEM) code for large-displacement analyses of the compliant structures.Simulation results from a displacement inverter indicated that the proposed representation scheme is appropriate.