Delphi应用程序与Microsoft Office的集成

本文论述Ｄelphi应用程序与Ｍicorsoft Office集成的原理并结合具体实例介绍几种Ｄelphi应用程序与Micorsoft Office集成的方法,重点介绍如何将Delphi的数据集ＤＡＴＡＳＥＴ的数据传送至Word文档或ＥＸＣＥＬ工作簿,形成新的工作表。     

量词在知识图中的分类与表示

在当今知识表示领域中,知识图作为自然语言理解的语义模型有其独到之处,而在自然语言处理中普遍认为词是最基本的单位,本文从语义学和自然语言处理的角度（主要是从知识图的角度,）在对介词的逻辑词的研究之后,按照量词图的结构,对汉语中的量词进行了分类,并且按照知识量词构造,给一词图。     

MPEG-4视频标准及其在可视电话中的应用

本文主要介绍了ＭＰＥＧ－４的视频标准和算法,并应用于可视电话,实现了ＭＰＥＧ－４的编解码,将ＭＰＥＧ－４的性能与Ｈ．２６３进行了多方面测试,对于图象分割问题,使用了矩形形状和差值形状,经过测试,有一定使用价值。     

超长指令字技术

指令系统是决定计算机体系结构特征的最核心因素,本文首先对近几年发展起来的超长指令字技术的基本原理进行了科短的介绍,进而Ｉｎｔｅｌ公司研制的ＩＡ－６４超长指令字计算机的设计描述了超长指令字计算机的性能特点,最后简述ＶＬＩＷ技术对计算机技术结构发展的影响及其最近发展概况。     

铁道钢轨断面的计算机图象处理与仿真

利用计算机图象处理和仿真技术,对铁道钢轨断面进行快速检测与处理的研究。     

对粘连和缺损数字串分割的研究

把缺损和粘连的字符串分割成单个字符以便后续识别,是字符识别的关键问题之一.本文以货运列车车号为例,提出一种有效的分割方法.该方法以弧特征和变基准线区域分割为基础,引入了识别反馈分割策略.将其应用于实际运行的识别系统时获得了较高的正确分割率.     

Efficient algorithms for segmentation of item-set time series

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets–Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.     

Disambiguation of partial cognates

Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. We propose a supervised and a semi-supervised method to disambiguate partial cognates between two languages: French and English. The methods use only automatically-labeled data; therefore they can be applied to other pairs of languages as well. The aim of our work is to automatically detect the meaning of a French partial cognate word in a specific context.

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.