基于拓扑优化的举升桥拖臂梁支架改良设计

本文指出了优化设计在目前产品设计领域的必要性;用数理统计的方法对多工况下应力最值方差作出评价,为多工况受力零件的设计评价及结果优化提供了数值依据。并以举升桥拖臂梁支架改良设计为实例,详细介绍了多工况下产品的优化设计方法与过程,达到了降低成本、提高产品质量的双重功效。

A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE

A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly.

Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation

The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed by sampling from 24-h-interval significant wave height differences of model outputs over a long period,and is validated with altimeter significant wave height data,indicating that the ensemble errors have nearly the same probability distribution function. The background error covariance fields expressed by the ensemble sampled are anisotropic. Updating the static samples by season,the seasonal characteristics of the correlation coefficient distribution are reflected. Hindcast experiments including assimilation and control runs are conducted for the summer of 2010 in the South China Sea. The effect of ensemble optimal interpolation assimilation on wave hindcasts is validated using different satellite altimeter data(Jason-1 and 2 and ENVISAT) and buoy observations. It is found that the ensemble-optimal-interpolation-based wave assimilation scheme for the South China Sea achieves improvements similar to those of the previous optimal-interpolation-based scheme,indicating that the practical application of this computationally cheap ensemble method is feasible.

Unknown parameter’s variance-covariance propagation and calculation in generalized nonlinear least squares problem

The unknown parameter’s variance-covariance propagation and calculation in the generalized nonlinear least squares remain to be studied now, which didn’t appear in the internal and external referencing documents. The unknown parameter’s vari- ance-covariance propagation formula, considering the two-power terms, was concluded used to evaluate the accuracy of unknown parameter estimators in the generalized nonlinear least squares problem. It is a new variance-covariance formula and opens up a new way to evaluate the accuracy when processing data which have the multi-source, multi-dimensional, multi-type, multi-time-state, different accuracy and nonlinearity.

Establishment and tests of EnOI assimilation module for WAVEWATCH Ⅲ

In this paper, we propose a parallel data assimilation module based on ensemble optimal interpolation （EnOI）. We embedded the method into the full-spectral third-generation wind-wave model, WAVEWATCH III Version 3.14, producing a wave data assimilation system. We present our preliminary experiments assimilating altimeter significant wave heights （SWH） using the EnOI-based wave assimilation system. Waters north of 15°S in the Indian Ocean and South China Sea were chosen as the target computational domain, which was two-way nested into the global implementation of the WAVEWATCH III. The wave model was forced by six-hourly ocean surface wind velocities from the cross-calibrated multi-platform wind vector dataset. The assimilation used along-track SWH data from the Jason-2 altimeter. We evaluated the effect of the assimilation on the analyses and hindcasts, and found that our technique was effective. Although there was a considerable mean bias in the control SWHs, a month-long consecutive assimilation reduced the bias by approximately 84% and the root mean-square error （RMSE） by approximately 65%. Improvements in the SWH RMSE for both the analysis and hindcast periods were more significant in July than January, because of the monsoon climate. The improvement in model skill persisted for up to 48 h in July. Furthermore, the SWH data assimilation had the greatest impact in areas and seasons where and when the sea-states were dominated by swells.

A COMPARISON OF TWO 2D CHANNEL ESTIMATORS FOR OFDM SYSTEM

The paper deals with two-dimensional （2D） channel estimation of Orthogonal Frequency Division Multiplexing （OFDM） system ill slow fading wireless channel. We concentrate on two channel estimation schemes： Least Square （LS）＋Weighted BiLinear （WBL） and LS＋Linear Minimum Mean-Squared Error （LMMSE） where the first method is proposed in this paper. After theory analysis and simulation in Typical Urban （TU） channel, we find that LS＋LMMSE achieves the optimal perform- anee by exploiting prior knowledge of channel whereas LS＋WBL, without requiring channel knowl- edge and with only half of the computational amount of LS＋LMMSE, approaches LS＋LMMSE in Bit Error Ratio （BER） performance when the distance of two adjoining pilot symbols along frequency direction is sufficiently small. This makes LS＋WBL very suitable for wideband wireless applications.

Joint Transceiver Design for OFDM Systems

Orthogonal Frequency Division Multiplexing(OFDM) is an effective technique to deal with a frequency selective channel since it can convert the channel into some flat fading subchannels.However,very different output SNR values of the subchannels will lead to poor bit error performance when a linear equalizer and Equal Bit Allocation(EBA) are adopted in OFDM systems.So,we proposed three novel nonlinear Joint Transceiver(JT) schemes based on Zero-Forcing(ZF) criterion and Minimum Mean Square Error(MMSE) criterion respectively,which can transform all subchannels of an OFDM system into subchannels with identical channel gain.Thus,EBA is equivalent to the Optimum Bit Allocation(OBA) for these subchannels.Numerical analysis helps us to obtain the theoretical approximate BER values of the JT scheme.Simulation results verify the numerical analysis and confirm that the performance of our proposed JT scheme greatly outperforms the traditional linear equalizer with EBA at moderate and high SNR values.

Asymptotic Behavior of Second Order Neutral Difference Equations with Maxima

The authors consider the following second order neutral difference equation with maxima △（αn△（yn＋pnyn-k））-qn max [n-l,n]ys=0,n=0,1,2,…,（＊）where {αn}, {pn} and （qn} are sequences of real numbers, and k and l are integers with k ≥ 1 and l 〉 0. And the asymptotic behavior of nonoscillatory solutions of （＊）. An example is given to show the difference between the equations with and without ＂maxima＂ is studied.

OPTIMAL MULTI-ASSET INVESTMENT WITH NO-SHORTING CONSTRAINT UNDER MEAN-VARIANCE CRITERION FOR AN INSURER

This paper considers the optimal investment strategy for an insurer under the criterion of mean-variance. The risk process is a compound Poisson process and the insurer can invest in a risk-free asset and multiple risky assets. This paper obtains the optimal investment policy using the stochastic linear quadratic （LQ） control theory with no-shorting constraint. Then the efficient strategy （optimal investment strategy） and efficient frontier are derived explicitly by a verification theorem with the viscosity solution of Hamilton-Jacobi-Bellman （HJB） equation.

Science Letters:A simplified MMSE-based iterative receiver for MIMO systems

A simplified minimum mean square error(MMSE) detector is proposed for joint detection and decoding of multi-ple-input multiple-output(MIMO) systems.The matrix inversion lemma and the singular value decomposition(SVD) of the channel matrix are used to simplify the computation of the coefficient of the MMSE filter.Compared to the original MMSE detector,the proposed detector has a much lower computational complexity with only a marginal performance loss.The proposed detector can also be applied to MIMO systems with high order modulations.

Superiority of empirical Bayes estimator of the mean vector in multivariate normal distribution

In this paper, the Bayes estimator and the parametric empirical Bayes estimator(PEBE) of mean vector in multivariate normal distribution are obtained. The superiority of the PEBE over the minimum variance unbiased estimator(MVUE) and a revised James-Stein estimators(RJSE) are investigated respectively under mean square error(MSE) criterion. Extensive simulations are conducted to show that performance of the PEBE is optimal among these three estimators under the MSE criterion.