基于线性预测残差倒谱的基音检测

传统的基音检测算法对含噪语音的检测结果较差,本文在线性预测倒谱方法的基础上采用基于线性预测残差倒谱的方法对含噪语音进行基音检测。实验结果表明,该方法使基音峰点更加清晰,提高了基音检测的精度。     

Mixed <Emphasis Type="Italic">H</Emphasis><Subscript>2</Subscript>/<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> filtering for a class of high-speed sampling uncertain systems

The problem of mixed H2/H∞ filtering for polytopic Delta operator systems is investigated. The aim is to design a linear asymptotically stable filter which guarantees that the filtering error system has different performances in different filtering channels. Based on a parameter-dependent Lyapunov function, a new mixed H2/H∞ performance criterion is presented. Upon this performance criterion, a sufficient condition for the full-order mixed H2/H∞ filter is derived in terms of linear matrix inequalities. The filter can be obtained from the solution of a convex optimization problem. The proposed filter design procedure is less conservative than the strategy based on the quadratic stability notion. A numerical example is given to illustrate the feasibility of the proposed approach.     

Neural-Based Learning Classifier Systems

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.     

Population-Based Incremental Learning With Associative Memory for Dynamic Environments

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments.     

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.     

NIST新HASH算法征集活动综述

随着SHA-1漏洞被发现,对新的HASH算法的需求日渐突出。NIST专门对此召开两次研讨会,并举办了新算法征集活动,旨在发展新的HASH函数。该文对有关活动进行了综述,特别是对新算法的质量要求进行了分析。     

蜂窝夹层板的非线性动力学研究

研究了正六角形蜂窝夹层板的非线性动力学问题．考虑高阶横向剪切变形和横向阻尼的影响,建立了面内激励和横向外激励联合作用下的四边简支蜂窝夹层板的非线性偏微分运动控制方程．综合运用Galerkin方法和数值方法,模拟不同激励作用下的混沌运动,得到二维相图、二维波形图和频谱图．研究结果表明：随着激励的增加,系统会重复呈现周期运动、混沌运动、周期运动的变化规律．     

变流速输液管的周期和混沌振动

研究了参数激励和外激励联合作用下输流管道的非线性振动问题．只考虑管道变形的几何非线性因素,利用Hamilton原理得到单侧受简谐均布载荷作用下输液管的非线性动力学方程,对系统运动偏微分方程综合运用多尺度法和Galerkin离散方法,得到了主参数共振-1／2亚谐共振和1：2内共振情况下的平均方程．数值模拟结果表明参数激励和外激励联合作用下的悬臂输液管呈现周期运动、多倍周期运动和混沌运动的变化规律．     

浅谈影像与平面设计学科交叉应用的现在与未来

本文在当前数字技术的空前发展与"读图时代"的到来使得影像与平面设计两学科的交叉互动关系空前紧密背景下,对影像与平面设计两者的结合及其现状进行了阐述。目的是同业界学者同仁与设计师们对该领域存在的问题予以分析研究和探讨;希望通过系统的梳理和研究,建立一个符合当今时代实际市场需求的学科交叉应用的理论架构与可操作的实践方法。     

一种面向领域的数据集成解决方案

作战指挥系统中数据来源各异,各子系统之间数据交换量大,数据的实时性,准确性要求高.针对作战指挥这一特定应用场合,提出一种数据集成解决方案,以提供统一的接口透明地访问各个异构数据源,并采用DDS以及XML缓冲数据库技术提高数据访问的涑度.