广义de Bruijn多计算机互连网络

（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ多计算机系统互连网络限制网络的规模必须为ｄ＾ｍ。本文把（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ网络推广到了任意顶点情形，提出了广义ｄｅ Ｂｒｕｉｊｎ网络，研究了广义ｄｅ Ｂｒｕｉｊｎ网络的自路由性质，设计了路由算法，研究了广义ｄｅ Ｂｒｕｉｊｎ网络顶点度的性质和容错特性。     

层次式级联多网络模型及其性能

本文提出了一种层次式级联多网络结构，论述了其训练算法，并验证了该结构的收敛性。这种结构在处理高阶非线性分布数据的分类问题方面具有优良的性能，用北京谱仪事例过滤进行性能测试结果表明，其训练和泛化性能都优于单个ＢＰ网络。     

一个高电阻值测量电路

介绍一种新的高阻测试方法，可满足对高达１×１０＾１６Ω的测试要求。测试结果显示，对于１×１０＾１３Ω重复误差优于±２％。高阻测试电路通过接口与６８０９微机配套使用，操作简便，读数直观，便于长时间重复测量。     

冗余技术在维持DCS系统稳定运行中的作用

以浙江中控JX300-XP为例,介绍冗余系统在系统长周期运行中的关键作用。     

Analysis of oceanaut operating performance using an integrated Bayesian network aided by the fuzzy logic theory

Because the oceanaut plays a significant role in safety and capability during manned deep-diving scientific tasks, preventing oceanaut performance decline is of paramount importance. However, the factors responsible for oceanaut performance are almost entirely unexplored. To address the preceding issues, a quantitative method of fuzzy integrated Bayesian network (FIBN) was modeled within the limits of oceanaut operating procedures. To quantify the probabilities of the influencing factors, the probability of each node in the FIBN was calculated using integrated expert judgement, fuzzy logic theory, and Bayesian network. By considering a total of 28 factors related to oceanaut performance in the “Jiaolong” manned submersible, this study found that difficult sampling, long sampling times, cabin equipment failure, oceanaut physical decline, and declining decision-making ability are important factors that affect oceanaut performance. The FIBN proposed in our study fused the qualitative and quantitative methods and can be developed into a versatile tool for analysis of comprehensive systems that contain both static and dynamic factors.Relevance to industryThe results provide a powerful basis for the design of manned submersible and assignment of tasks to oceanauts, while the fuzzy integrated Bayesian network (FIBN) method proposed can be effectively applied to various quantitative assessment fields which direct researchers to deal with analysis problems of complex systems.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

The pulse distributed file system

A network of powerful personal computers, linked by a high-speed local area network, is being seen increasingly as an alternative to a traditional centralized time-sharing operating system. The PULSE project is investigating how such a system may be constructed to give the benefits of a self-sufficient personal computer to each user without losing the facilities for communication and sharing of data inherent in centralized systems. In particular, a distributed file system has been built which provides a single global UNIX

1 UNIX is a trademark of Bell Laboratories.

-like hierarchy, with a consistent appearance when accessed from any machine. Replicated copies of files are maintained to improve reliability, increase performance, and enable each machine to run stand-alone, albeit with reduced facilities.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

利用C++Builder对处理ESCR数据方法的改进

介绍一种利用C＋＋Builder编程工具自动处理聚乙烯环境应力开裂(ESCR)试验数据的方法。此方法利用计算机简化试验数据的处理过程,消除标准试验方法中作图法所引入的人为误差,并可以大幅度缩短试验时间。     

A new approach of audio emotion recognition

A new architecture of intelligent audio emotion recognition is proposed in this paper. It fully utilizes both prosodic and spectral features in its design. It has two main paths in parallel and can recognize 6 emotions. Path 1 is designed based on intensive analysis of different prosodic features. Significant prosodic features are identified to differentiate emotions. Path 2 is designed based on research analysis on spectral features. Extraction of Mel-Frequency Cepstral Coefficient (MFCC) feature is then followed by Bi-directional Principle Component Analysis (BDPCA), Linear Discriminant Analysis (LDA) and Radial Basis Function (RBF) neural classification. This path has 3 parallel BDPCA + LDA + RBF sub-paths structure and each handles two emotions. Fusion modules are also proposed for weights assignment and decision making. The performance of the proposed architecture is evaluated on eNTERFACE’05 and RML databases. Simulation results and comparison have revealed good performance of the proposed recognizer.