地震资料预测孔隙度的方法与分析

本文介绍用地震资料预测孔隙度的几种方法,以及国内外预测孔隙度的现状,并指出部分地震资料预测孔隙度存在的问题,也指出今后该方法预测隙度的研究方向,同时提出个人对今后预测方法的看法。     

一类时滞系统的模糊预测控制方法

对模糊控制和预测控制相结合的控制方法进行研究,提出了一种简单易于实现的模糊预测控制的组合方法.此方法将模糊控制的定性分析特点和预测控制的定量分析特点有效地结合起来,同时积分器的引入可以消除稳态偏差.仿真结果表明,该方法对控制作用的滞后时间不确定的系统十分有效.     

GFMIMU/GPS组合导航系统信息融合技术研究

GFMIMU/GPS(Gyroscope Free Micro Inertial Measurement Unit/Global Positioning System)组合导航系统具有抗高g值冲击、低成本、长寿命、较高精度等优点,在低成本精确制导武器和微小型无人机具有广阔的应用前景。针对工程应用中信息融合的精度和实时性两方面的要求,运用基于模型误差预测的扩展卡尔曼滤波MEP-EKF(Extended Kalman Filter base on Model Error Predictive)方法,将MEMS(Micro Electromechanical Systems)加速度计的误差作为模型误差来考虑,对GFMIMU/GPS组合导航系统进行建模仿真,将其与EKF和UKF(Unscented Kalman Filter)方法进行了仿真比较,在方位误差角的估计上取得了比他们精度高的仿真结果,而且MEP-EKF所需时间是UKF的10%.     

多元回归分析在油气产能预测中的应用

本论文利用多元回归分析对油气产能进行预测,并对多元回归分析的思想、原理进行了阐述;对油田产能预测结果进行了检验及精度估计,认为预测结果具有很好的指导意义。因此在油田生产中建议采用多元回归分析预测油气产能。     

蛋白质凝聚结晶和致病的动力学机制

这本书涉及了蛋白质在溶液中的相转变、自组装和聚集行为等问题。其主要目的是向跨学科的读者介绍这一领域的研究现状。书中主要讨论了蛋白质优质结晶的问题，指出其瓶颈在于晶体的成核。由于蛋白质的结构决定了它的功能，因此非常有必要培养出优质的晶体来确定其结构，其中遇到的主要挑战是确定具备最佳成核条件的溶液初始状态。     

论文更正及预测

《论随机量子空间及有关问题》(以下简称原论文)在本刊总第4期发表后,作者发现一处错误,现予以更正;并在更正理论的基础上,预测—标量粒子的质量。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

一种新的基于Kohonen网络的图像矢量量化

矢量量化是一种重要的数据压缩方法.本文利用Kohonen自组织映射神经网络进行矢量量化,首次较为详细地讨论了具体实现的步骤与细节,并在此基础上为改善边缘质量,提出一种基于Laplas算子检测边缘弓引导神经网络训练的方法,并通过实验证明其效果是明显的.