An original approximate method for estimating the invariant probability distribution of a large class of multi-dimensional nonlinear stochastic oscillators

This paper proposes an original method for obtaining analytical approximations of the invariant probability density function of multi-dimensional Hamiltonian dissipative dynamic systems under Gaussian white noise excitations, with linear non-conservative parts and nonlinear conservative parts. The method is based on an exact result and a heuristic argument. Its pertinence is attested by numerical tests.     

Characteristic Features of the Optimal Processing of the Results of Measurements in the Case of a Random Observation Function

An analysis is made of the characteristic features and problems of the optimal processing of the results of measurements in the case of a random observation function utilizing a nonlinear Kalman filter. A method is proposed for increasing the convergence domain of the filter with additional processing of measurements in the frequency domain.     

运行电站爆破震动控制研究

结合大渡河龚咀水电站下游河床整治爆破开挖拓宽，重点研究了爆破震动对运行电站机组及其仪表的影响，探讨了爆破震动安全判据和控制标准，用了若干实际工程采用的经验指标进行对比分析，所得结论意见可供同类工程作为爆震安全控制参考。     

双频抽运提高SBS相位共轭保真度

相位共轭保真度是相位共轭镜技术用于强激光系统中的一个重要指标。采用四波混频加强SBS的方法 ,其相干拍频驱动声子场 ,能提高SBS相位共轭保真度。对于双频单池结构 ,使用列阵相机测量激光束能量远场角分布 ,在SBS转换效率最佳时 ,对相干驱动SBS相位共轭保真度进行了实验研究     

一种实现最佳用户检测的非线性优化神经网络

本文提出并讨论了实现码分多址（CDMA）系统上最佳多用户检测（MUD）的一种神经网络方法。该方法通过将最佳多用户检测视为非线性优化组合问题，利用神经网络能有效求解非线性优化问题的优势，导出了一种非线性优化神经网络来实现最佳多用户检测，理论分析和计算机模拟表明，所提出的神经网络具有可实时应用的动态性能和较传统方法优越得多的误码率性能和抗多址干扰的性能。     

δ－Al_2O_3纤维／Al－12Si复合材料室温拉伸强度分析

对δ－Ａｌ２Ｏ３纤维／Ａｌ－１２Ｓｉ复合材料室温拉伸强度的分析表明，在实验条件下该复合材料存在δ－Ａｌ２Ｏ３纤维的最小体积分数Ｖｍｉｎ和临界体积分数Ｖｃｒｉｔ，并求出其基体强度σ＇ｍ和室温强度σｃ－δ－Ａｌ２Ｏ３纤维体积分数Ｖｆ直线方程及纤维的临界长度ｌｃ和复合材料的剪切应力τｉ．确定复合材料的ＲＯＭ预测曲线，应首先判断σ＇ｍ是否等于未增强合金的强度σｕｍ才能得出正确的结论     

精细功能陶瓷的若干发展趋势

概括了分析自９０年代初以来精细功能陶瓷的发展趋势，提出了一些相关的研究领域。     

有机极化聚合物材料及电光调制器件

有机极化聚合物材料是一类新型的功能高分子材料，在光电子及集成光学方面具有诱人的应用前景，其电光调制器件已接近实用化，本文综述了有机极化聚合物材料，极化聚合物体系及电光调制器件的研究进展，并指出它们存在的问题和发展方向。     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

测井新技术在四川油气勘探开发中的应用

综述随着四川盆地油气勘探深入所面临的油气领域复杂和困难的技术问题，介绍应用测井新技术为四川油气开发解决了大量的技术难题，特别是在复杂缝，洞型储层的评价，井旁地质构造形态精细分析和地质，测井，地震多学科结合储层横向预测等3个方面所取得的良好应用效果。