maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams

IEEE

an integrity assurance mechanism for run-time programs

Chinese Assoc Cryptol Res, State Key Lab Informat Secur, Inst Software, Grad Univ Chinese Acad Sci, Natl Nat Sci Fdn China

a computational proof of complexity of some restricted counting problems

South Central University

DECODE: a new method for discovering clusters of different densities in spatial data

National Key Basic Research and Development Program of China [2006CB701305]; State Key Laboratory of Resource and Environment Information System [088RA400SA]; Chinese Academy of Sciences

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

小波变换理论应用进展

本文介绍了小波变换理论 ,讨论了基本小波函数的选取准则和小波变换算法 ,分析了小波变换与人工智能等其它方法的结合方式和特点 .通过介绍小波变换在信号瞬态分析、图像边沿检测、图像去噪、模式识别、数据压缩、分形信号分析等方面的应用实例 ,讨论了小波变换在处理非平稳信号和复杂图像时的优势 .最后 ,对小波变换理论的发展及其应用前景作了描述 .

多机器人动态编队的强化学习算法研究

在人工智能领域中 ,强化学习理论由于其自学习性和自适应性的优点而得到了广泛关注 随着分布式人工智能中多智能体理论的不断发展 ,分布式强化学习算法逐渐成为研究的重点 首先介绍了强化学习的研究状况 ,然后以多机器人动态编队为研究模型 ,阐述应用分布式强化学习实现多机器人行为控制的方法 应用SOM神经网络对状态空间进行自主划分 ,以加快学习速度 ;应用BP神经网络实现强化学习 ,以增强系统的泛化能力 ;并且采用内、外两个强化信号兼顾机器人的个体利益及整体利益 为了明确控制任务 ,系统使用黑板通信方式进行分层控制 最后由仿真实验证明该方法的有效性

基于CORBA的多agent供应链管理系统的研究

本文从现代供应链管理概念及要求出发 ,指出协调与合作在供应链管理中的重要作用 ,应用分布式人工智能理论及分析方法 ,提出了基于多 agent的供应链管理结构 ,定义了各功能agent的作用和职能 ,研究了基于多 agent的供应链管理协作内容、特点及解决方法 ,介绍了基于CORBA的分布式系统开发方法 ,提出了基于 CORBA规范及 KQML协议的支持供应链管理协作研究及开发的软件结构 .

协作机器人学的研究现状与发展

随着机器人向系统应用的方向发展，提出了由多机器人构成的群体或社会的组织与控制问题．多机器人协作问题已成为机器人学研究领域的热点之一．其中基于分布式人工智能中多智能体系统理论，研究多机器人协作问题正受到普遍关注．本文对协作机器人学的研究现状进行了综述，并展望了其未来的发展。

三维物体识别及姿态测定的推理系统

本文介绍了三维物体识别及姿态测定的一种新技术，从物体空间域模型出发，通过约束推理及几何推理，在物体三维信息部分给定的条件下，推断预测图象模型，并通过实测的图象数据反馈，推断出隐含在图象中未给定的三维信息，最终实现三维物体识别及姿态测定。整个系统在ＶＩＣＯＭ机上用Ｃ语言完成。

基于MAX197的电量测控系统

利用单片机技术设计多路电量测控系统,实现对电量的精确测量、控制,同时输出人工智能或位式开关控制信号。系统以单片机为核心,利用模数转换器MAX197构成多路测量电路,利用放大器构成输出电路,实现多路一一对应的闭环测量控制。系统软件采用PID控制器。实践表明:可根据需要增减系统信号采样通道的数目,采用软件抗干扰措施,采样数据可靠性高。采用参数密码保护和自检系统,有效防止由于错误控制而引起的不可预知的甚至危险的后果。输入输出硬件结构简单,系统低成本高速度,具有较好的测量控制精度。

河流-三角洲储层剩余油分布模式及预测

Aim at the variousness and complexity of the spatial distribution of Remaining Oil in the fluvial and delta facies reservoir in paper. For example, in the La-Sa-Xing oilfield of Daqing, based on the research of the control factor and formation mechanization of block, single layer, interlayer and micromechanism, synthesizing the theories and methods of geology, well logging, reservoir engineering, artificial intelligence, physical simulation test , and computer multidisciplinary; Fully utilizing the material of geology, well logging, core well, dynamic monitor of oil and water well, and experimental analysis, from macro to micro, from quality to quantity, from indoor to workplace, we predicted the potentiality and distribution according to the four levels of Block, single layer, interlayer and micromechanism, and comprehensively summarized the different distribution pattern of remaining oil in the fluvial and delta facies reservoir This paper puts forward an efficient method to predict the remaining recoverable reserves by using the water flooding characteristic curve differential method and neutral network; for the first time utilizes multilevel fuzzy comprehensive judgment method and expert neutral network technology to predict the remaining oil distribution in the single layer? comprehensively takes advantage of reservoir flowing unit, indoor physical simulation test, inspection well core analysis and well-logging watered-out layer interpretation to efficiently predict the distribution of remaining oil; makes use of core analysis of different periods and indoor water driving oil test to study the micro distribution of remaining oil and the parameters varying law of reservoir substance properties, rock properties, wetting properties. Based on above, the remaining oil distribution predicting software is developed, which contains four levels of block, single layer, interlayer and micromechanism. This achievement has been used inLa-Sa-Xing oil field of Daqing and good results have been received.