IMP分散式数据采集系统及其S-网络的应用

描述了IMP(IsolatedMeasurementPod)分散式数据采集系统及S-网络的组成和工作原理,分析了它在微机实时监测及在线管理中的应用特点及其前景。

IMP分散式数据采集系统及其S-网络的应用

文章描述了ＩＭＰ（ＩｓｏｌａｔｅｄＭｅａｓｕｒｅｍｅｎｔＰｏｄ）分散式数据采集系统及Ｓ－网络的组成和工作原理。

IMP S-网络接口通信可靠性的改进

由于在S-网络上的IMPS-网络接口在线路上存在自身的缺陷，住往使其与主控计算机之间的通信受阻。本文从线路上改进了IMPS-网络接口，从而使通信更加稳定、可靠。

S-分布时滞神经网络概周期解的全局渐近稳定性

利用不动点理论和微分不等式技巧研究了S-分布时滞局域递归神经网络模型的概周期解,给出了概周期解存在性和全局渐近稳定性的充分条件。

INTELLIGENT FUSION FOR AEROENGINE WEAR FAULT DIAGNOSIS

Four common oil analysis techniques, including the ferrography analysis （FA）, the spectrometric oil analysis （SOA）, the particle count analysis （PCA）, and the oil quality testing （OQT）, are used to implement the military aeroengine wear fault diagnosis during the test drive process. To improve the precision and the reliability of the diagnosis, the aeroengine wear fault fusion diagnosis method based on the neural networks （NN） and the Dempster-Shafter （D-S） evidence theory is proposed. Firstly, according to the standard value of the wear limit, original data are pre-processed into Boolean values. Secondly, sub-NNs are established to perform the single diagnosis, and their training samples are dependent on experiences from experts. After each sub-NN is trained, diagnosis results are obtained. Thirdly, the diagnosis results of each sub-NN are considered as the basic probability allocation value to faults. The improved D-S evidence theory is applied to the fusion diagnosis, and the final fusion results are obtained. Finally, the method is verified by a diagnosis example.

一种新的大型水电站计算机监测系统

本文介绍了一个用IBM—PC/×T286和IMP构成的新型水电站计算机监测系统.该系统打破了IMP的常规使用模式,对一块模拟量 IMP 的输入通道扩展后作数据采集.一块IMP代替多块IMP的功能,从而使系统费用成倍降低.用一块数字量IMP作扩展控制和数字量检测.采用了先进的QUICK BASIC编译软件.因此该系统精度高、抗干扰性能强.使用方便、成本低.

IMP数据采集系统在电站性能试验中的应用

本文介绍了分散式数据采集系统的特点及系统的软硬件组成,结合实例分析了它在大型电站性能考核试验任务中的应用。

分散式数据采集系统在汽机性能试验中的应用

介绍了分散式数据采集系统IMP及S-网络的组成、特点和工作原理,叙述了分散式数据采集系统的使用体会,分析了它在大型电站汽轮机性能试验中的应用特点及其发展前景。

N-S EQUATION CALCULATIONS ON MULTI-ELEMENT AIRFOILS WITH ZONAL PATCHED GRIDS

For a complex flow about multi-element airfoils a mixed grid method is set up. C-type grids are produced on each element′s body and in their wakes at first, O-type grids are given in the outmost area, and H-type grids are used in middle additional areas. An algebra method is used to produce the initial grids in each area. And the girds are optimized by elliptical differential equation method. Then C-O-H zonal patched grids around multi-element airfoils are produced automatically and efficiently. A time accurate finite-volume integration method is used to solve the compressible laminar and turbulent Navier-Stokes (N-S) equations on the grids. Computational results prove the method to be effective.

A Multilayer Recurrent Fuzzy Neural Network for Accurate Dynamic System Modeling

A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.

Application of data fusion method to fault diagnosis of nuclear power plant

The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosisinto three levels, which are data fusion level, feature level and decision level. The feature leveluses three parallel neural networks whose structures are the same. The purpose of using neuralnetworks is mainly to get basic probability assignment ( BPA) of D-S evidence theory, and the neuralnetworks in feature level are used for local diagnosis. D-S evidence theory is adopted to integratethe local diagnosis results in decision level. The reactor coolant system is the study object andwe choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example.The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirementof complicated system, and verify that the fusion fault diagnosis system can realize the faultdiagnosis of NPP on line timely.

EARLY WARNING MODEL OF NETWORK INTRUSION BASED ON D-S EVIDENCE THEORY

Application of data fusion technique in intrusion detection is the trend of next- generation Intrusion Detection System (IDS). In network security, adopting security early warn- ing technique is feasible to effectively defend against attacks and attackers. To do this, correlative information provided by IDS must be gathered and the current intrusion characteristics and sit- uation must be analyzed and estimated. This paper applies D-S evidence theory to distributed intrusion detection system for fusing information from detection centers, making clear intrusion situation, and improving the early warning capability and detection efficiency of the IDS accord- ingly.

Full Diversity Reception Based on Dempster-Shafer Theory for Network Coding with Multiple-Antennas Relay

In this paper,a two-way relay system which achieves bi-directional communication via a multiple-antenna relay in two time slots is studied.In the multiple access(MA) phase,the novel receive schemes based on Dempster-Shafer(D-S) evidence theory are proposed at the relay node.Instead of traditional linear detection,the first proposed MIMO-DS NC scheme adopts D-S evidence theory to detect the signals of each source node before mapping them into network-coded signal.Moreover,different from traditional physical-layer network coding(PNC) based on virtual MIMO model,the further proposed MIMO-DS PNC comes from the vector space perspective and combines PNC mapping with D-S theory to obtain network-coded signal without estimating each source node signal.D-S theory can appropriately characterize uncertainty and make full use of multiple evidence source information by Dempster's combination rule to obtain reliable decisions.In the broadcast(BC) phase,the space-time coding(STC) and antenna selection(AS) schemes are adopted to achieve transmit diversity.Simulation results reveal that the STC and AS schemes both achieve full transmit diversity in the BC phase and the proposed MIMO-DS NC/PNC schemes obtain better end-to-end BER performance and throughputs compared with traditional schemes with a little complexity increasing and no matter which scheme is adopted in the BC phase,MIMO-DS PNC always achieves full end-to-end diversity gain as MIMO-ML NC but with a lower complexity and its throughput approaches the throughput of MIMO-ML NC in high SNR regime.

Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory

According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.

Self-assembled three-dimensional carbon networks with accessorial Lewis base sites and variational electron characteristics as efficient oxygen reduction reaction catalysts for alkaline metal-air batteries

Heteroatom-doped carbon has been demonstrated to be one of the most promising non-noble metal catalysts with high catalytic activity and stability through the modification of the electronic and geometric structures.In this study,we develop a novel solvent method to prepare interconnected N,S co-doped three-dimensional（3D）carbon networks with tunable nanopores derived from an asso-ciated complex based on melamine and sodium dodecylbenzene sulfonate（SDBS）.After the intro-duction of silica templates and calcination,the catalyst exhibits 3D networks with interconnected 50-nm pores and partial graphitization.With the increase of the number of Lewis base sites caused by the N doping and change of the carbon charge and spin densities caused by the S doping,the designed N,S co-doped catalyst exhibits a similar electrochemical activity to that of the commercial 20-wt%Pt/C as an oxygen reduction reaction catalyst.In addition,in an aluminum-air battery,the proposed catalyst even outperforms the commercial 5-wt%Pt/C catalyst.Both interconnected porous structures and synergistic effects of N and S contribute to the superior catalytic perfor-mance.This study paves the way for the synthesis of various other N-doped and co-doped carbon materials as efficient catalysts in electrochemical energy applications.

The REMO Ocean Data Assimilation System into HYCOM(RODAS_H):General Description and Preliminary Results

The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.

A research on an energy-saving software for pumping units based on FNN intelligent control

An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.

Nuclear uncertainties in the s-process simulation

The heavy elements in the Universe are formed during the s- and r-processes mainly in AGB stars and supernovae, respectively. Simulation of s- and r-nucleosynthesis critically depends on the neutron capture and weak decay rates for all the nuclei on the reaction chain. The present work analyzes systematically the neutron capture rates (cross sections) for the s-process nuclei, including ～3000 rates on ～200 nuclei. The network calculations for the constant temperature s-process have been performed using the different data sets selected as the nuclear inputs to investigate the uncertainties in the predicted s-abundances. We show that the available cross sections of neutron capture on many s-process nuclei still carry large uncertainties, which lead to low accuracy in the determination of s-process isotope abundances. We analyze the neutron capture cross section data for the same unique isobar nucleus accorded by year from previous work. Such an analysis indicates that the s-process has been studied for more than fifty years and there exist two research stages around 1976 and 2002, respectively. The needs and opportunities for future experiments and theoretical tools are highlighted to remove the existing shortcomings in the neutron capture rates.

Tsallis Mapping in Growing Complex Networks with Fitness

We introduce Tsallis mapping in Bianconi-Barabgsi （B-B） fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics, which is characterized by a dimensionless nonextensivity parameter q. It is found that this new phenomenological parameter plays an important role in the evolution of networks： the underlying evolving networks may undergo a different phases depending on the q exponents, comparing to the original B-B fitness model, and the corresponding critical transition temperature Tc is also identified.