方向网优化设计中定向角的特殊处理

本文针对方向网优化设计中定向角的特殊处理,简述了四种方法,并从平差和优化设计两者的要求出发,提出了投影矩阵法,详细论证了相应的公式及计算可行性。

Modeling of Intelligent Integrated Network Management System Using Object Oriented Approach *

The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.

Energy-Efficient Transceiver Design for Multi-Pair Two-Way Relay Systems

In this paper, we focus on energy-efficient transceiver and relay beamforming design for multi-pair two-way relay system. The multi-antenna users and the multi-antenna relay are considered in this work. Different from the existing works, the proposed algorithm is energy-efficient which is more applicable to the future green network. It considers both the sum-MSE problem and the power consumption problem for the users under the relay power constraint. Based on the optimal condition decomposition(OCD) method, the energy-efficient precoders at the users can be designed separately with limited information exchanged. The proposed relay beamforming algorithm is based on the alternative direction method of multipliers(ADMM) which has simpler iterative solution and enjoys good convergence. Simulation results demonstrate the performance of the proposed algorithms in terms of power consumption and MSE performance.

On-Cam Operation of Counter-Rotating Type Hydroelectric Unit

The counter-rotating type hydroelectric unit, which is composed of the axial flow type tandem runners and the peculiar generator with double rotational armatures, has been proposed. In the unit, the front and the rear runners counter-drive the inner and the outer armatures of the generator, respectively. Besides, the flow direction at the rear runner outlet must coincide with the direction at the front runner inlet, because the angular momentum change through the rear runner must coincides with that through the front runner. In this paper, the tandem runners work at on-cam conditions in keeping the induced frequency constant, to provide the hydroelectric unit for the power grid system. The output and the hydraulic efficiency are affected by the adjusting angles of the front and the rear blades. Both optimum angles giving the maximum output or efficiency were presented at the various discharge/head circumstances, accompanying with the turbine performances.

Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance

Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Spectrum allocation game of directional transmission in multi-hop cognitive radio networks

The spectrum allocation for links in multi-hop cognitive radio networks is addressed.The links rent the vacant licensed bands offered by primary users for implementing directional transmission.To minimize the individual cost,the links share the licensed band and rental fee.An interference model for the directional transmission in cognitive radio networks is proposed to formulate the cooperative and dynamic behavior of the links using the theory of hedonic game,called spectrum allocation game.The game is proved to converge to the core stable state indicating that all links satisfy with their current conditions and do not deviate from their coalitions.Numerical results show that the game improves spectral efficiency and contributes to reducing the individual cost of the links.

The Effect of Netting Solidity Ratio and Inclined Angle on the Hydrodynamic Characteristics of Knotless Polyethylene Netting

Knotless polyethylene(PE) netting has been widely used in aquaculture cages and fishing gears, especially in Japan. In this study, the hydrodynamic coefficient of six knotless PE netting panels with different solidity ratios were assessed in a flume tank under various attack angles of netting from 0?(parallel to flow) to 90?(perpendicular to flow) and current speeds from 40 cm s^(-1) to 130 cm s^(-1). It was found that the drag coefficient was related to Reynolds number, solidity ratio and attack angle of netting. The solidity ratio was positively related with drag coefficient for netting panel perpendicular to flow, whereas when setting the netting panel parallel to the flow the opposite result was obtained. For netting panels placed at an angle to the flow, the lift coefficient reached the maximum at an attack angle of 50? and then decreased as the attack angle further increased. The solidity ratio had a dual influence on drag coefficient of inclined netting panels. Compared to result in the literature, the normal drag coefficient of knotless PE netting measured in this study is larger than that of nylon netting or Dyneema netting.

Artificial Neural Network Model for Optical Fiber Direction Coupler Design

A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time saving. So it is promising in the field of both investigation and application.

Study on the comprehensive advantage evaluation method of high-tech enterprises based on RBF artificial neural network

This paper designs an intelligent evaluation approach using a Radial Basis Function （RBF） Artificial Neural Network. We based our approach on establishing a comprehensive advantage evaluating index system that offers scientific substance for creating a development plan and the strategic management of high-tech industry and regional clusters of high-tech enterprises. Furnhermore, this paper selects some typical high-tech enterprises＇ data to make comprehensive training on the network system. Meanwhile, the paper chooses some enterprises as testing samples to test the method, the result of which proves that this method is truly effective. The research of this paper provides a comprehensive advantage evaluating and managing method for high-tech enterprise.

Preliminary Study on Seismic Anisotropy in the Crust in the Northeast of Hainan

With SAM shear-wave splitting analysis,shear-wave splitting parameters at two stations of the digital seismic network in the northeast of Hainan are obtained based on the data from the Hainan Digital Seismic Network from 2000 to 2013. The results show that the predominant polarization direction of fast share-wave represents the direction of in-situ maximum principal compressive stress. The predominant polarizations of Qixingling( QXL) seismic station are in the NEE direction,which is different from the direction of principal compressive stress of the Hainan area,but same as the strikes of faults in the NE direction,which means that the local tectonics and stress fields are complicated. The predominant polarization of Qingshanling( QSL) seismic station is in the NNE-NS direction,which indicates the tectonic significance of the strikes of NNE-trending faults.At the same time,the study confirms that the predominant polarizations of the stations located on active faults or at the junctions of several active faults are parallel to the strikes of faults which control the earthquakes used in this analysis, and the predominant polarizations are scattered,which indicates the complicated background of fault structures and stress distribution.

A NOVEL INDOOR GEO-LOCATION METHOD USING MIMO ARRAY

In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with multiple antennas, to make novel indoor geo-location method possible. In this paper, we presented a novel Least Square Support Vector Machine (LS-SVM) based data fusion algorithm to fuse signal strength measurements for indoor geo-location using only a single AP with MIMO arrays. We evaluate our proposed algorithms under indoor environments by MATLAB simulations. Simulation results show that our MIMO-based algorithm is superior to conventional least square algorithm.

Developing energy forecasting model using hybrid artificial intelligence method

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

LOCAL STABILITY AND BIFURCATION IN A THREE-UNIT DELAYED NEURAL NETWORK

A system of three-unit networks with coupled cells is investigated.The general formula for bifurcation direction of Hopf bifurcation is calculated and the estimate formula of period of the periodic solution is given.

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.

Anisotropic mesh generation methods based on ACVT and natural metric for anisotropic elliptic equation

Anisotropic meshes are known to be well-suited for problems which exhibit anisotropic solution features. Defining an appropriate metric tensor and designing an efficient algorithm for anisotropic mesh gen- eration are two important aspects of the anisotropic mesh methodology. In this paper, we are concerned with the natural metric tensor for use in anisotropic mesh generation for anisotropic elliptic problems. We provide an algorithm to generate anisotropic meshes under the given metric tensor. We show that the inverse of the anisotropic diffusion matrix of the anisotropic elliptic problem is a natural metric tensor for the anisotropic mesh generation in three aspects： better discrete algebraic systems, more accurate finite element solution and superconvergence on the mesh nodes. Various numerical examples demonstrating the effectiveness are presented.

AN ANISOTROPIC NONCONFORMING FINITE ELEMENT SCHEME WITH MOVING GRIDS FOR PARABOLIC INTEGRO-DIFFERENTIAL EQUATIONS

A Crank-Nicolson scheme based on nonconforming finite element with moving grids is dis- cussed for a class of parabolic integro-differential equations under anisotropic meshes. The corresponding convergence analysis is presented and the error estimates are obtained by using the interpolation operator instead of the conventional elliptic projection which is an indispensable tool in the convergence analysis of traditional finite element methods in previous literature.

Task space control of free-floating space robots using constrained adaptive RBF-NTSM

Trajectory tracking control of space robots in task space is of great importance to space missions, which require on-orbit manipulations. This paper focuses on position and attitude tracking control of a tree-floating space robot in task space. Since nei- ther the nonlinear terms and parametric uncertainties of the dynamic model, nor the external disturbances are known, an adap- tive radial basis function network based nonsingular terminal sliding mode （RBF-NTSM） control method is presented. The proposed algorithm combines the nonlinear sliding manifold with the radial basis function to improve control performance. Moreover, in order to account for actuator physical constraints, a constrained adaptive RBF-NTSM, which employs a RBF network to compensate for the limited input is developed. The adaptive updating laws acquired by Lyapunov approach guar- antee the global stability of the control system and suppress chattering problems. Two examples are provided using a six-link free-floating space robot. Simulation results clearly demonstrate that the proposed constrained adaptive RBF-NTSM control method performs high precision task based on incomplete dynamic model of the space robots. In addition, the control errors converge faster and the chattering is eliminated comparing to traditional sliding mode control.

A Combination Method for Wind Power Prediction Based on Cooperative Game Theory

Accurate prediction of wind power is significant for power system dispatching as well as safe and stable operation. By means of BP neural network, radial basis function neural network and support vector machine, a new combined method of wind power prediction based on cooperative game theory is proposed. In the method, every single forecasting model is regarded as a member of the cooperative games, and the sum of square error of combination forecasting is taken as the result of cooperation. The result is divided among the members according to Shapley values, and then weights of combination forecasting can be obtained. Application results in an actual wind farm show that the proposed method can effectively improve prediction precision.