全天棚送风相对两侧墙回风式洁净室特性研究

对全天棚送风相对两侧墙下部回风式洁净室的空态工况下的气流流型以及污染物分布特性进行了模拟，并将模拟结果与已有实验，实测结果进行了比较，分析，提出一些关于这种洁净室的新观点。     

汉字识别的方法及Rosen细化算法的改进

介绍了汉字识别方法，改进了Ｒｏｓｅｎ细化算法，消除了该算法中的笔道丢失与断开     

“类似形”在截交线教学中的应用

为搞好截交线一章的教学,我们进行了教学改革,把线面分析法提前“渗透”到该章的教学中。文中举例说明了“类似形”的具体应用。     

一个采用Client/Server方式基于WWW 的多用户交流讨论系统

文章介绍了一种采有Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ方式工作的基于ＷＷＷ的多用户交流讨论系统，它比一般的基于Ｔｅｌｎｅｔ方式的ＢＢＳ，具有通讯效率高，媒体介质丰富，用户界面善等特点，更适合于Ｉｎｔｅｒｎｅｔ上交流。     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

Incremental Feature Selection

Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.     

On Pattern Frequency Occurrences in a Markovian Sequence

Consider a given pattern H and a random text T generated by a Markovian source. We study the frequency of pattern occurrences in a random text when overlapping copies of the pattern are counted separately. We present exact and asymptotic formulae for moments (including the variance), and probability of r pattern occurrences for three different regions of r , namely: (i) r=O(1) , (ii) central limit regime, and (iii) large deviations regime. In order to derive these results, we first construct certain language expressions that characterize pattern occurrences which are later translated into generating functions. We then use analytical methods to extract asymptotic behaviors of the pattern frequency from the generating functions. These findings are of particular interest to molecular biology problems (e.g., finding patterns with unexpectedly high or low frequencies, and gene recognition), information theory (e.g., second-order properties of the relative frequency), and pattern matching algorithms (e.g., q -gram algorithms).     

An Analytical Method for Predicting the Performance of Parallel Image Processing Operations

This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a large set of image processing operations running on a p-processor parallel system. The model which requires only a few parameters obtainable on a minimal system can help in the systematic design, evaluation and performance tuning of parallel image processing systems. Using the model one can reason about the performance of a parallel image processing system prior to implementation. The method can also support programmers in detecting critical parts of an implementation and system designers in predicting hardware performance and the effect of hardware parameter changes on performance. The execution of parallel image processing operations was studied and operations were arranged in three main problem classes based on data locality and the communication patterns of the algorithms. The core of the method is the derivation of the overhead function, as it is the overhead that determines the achievable speedup. The overheads were examined and modelled for each class. The use of the method is illustrated by four class-representative image processing algorithms: image-scalar addition, convolution, histogram calculation and the Fast Fourier Transform. The developed performance model has been validated on a 16-node parallel machine and it has been shown that the model is able to predict the parallel run-time and other performance metrics of parallel image processing operations accurately.     

基于Internet和多传感器融合的工厂自动化专家网络

本文介绍了在未来网络化时代工业自动化中的一种基本技术框架－基于Ｉｎｔｅｒｎｅｔ环境和多传感器融合的专家网络。基于模糊逻辑和要素空间理论，我们试图开发适于各种工业应用的通用软件平台和硬件结构。系统是一种适合于工业自动化中的专家系统的专家机。人们使用ＨＴＭＬ和ｓｍａｒｔ ａｐｐｌｅｔｓ（用Ｊａｖａ语言编写的模糊逻辑算法小应用程序）来实现知识的获取，知识库的建立，知识的共享以及实时问题的解决。     

P＋P：同步时序电路的并行码和并行故障模拟器

开发的一个新的快速故障模拟器Ｐ＋Ｐ。该模拟器使用了并行码与并行故障模拟算法，实现了同步时序电路故障模拟的两路并行性，采用了全局故障分组，锥形操作，电路级化及改进的组号ＩＤ等技术。Ｐ＋Ｐ已在ＳＵＮ ＳＰＡＲＣ－２工作站上实现，运行了大部分的ＩＳＣＡＳ Ｂｅｎｃｈｍａｒｋ同步时序电路。最后给出了实验结果。