Characteristics and classification of high-current fault arcs on distribution systems

When a fault occurs on transmission or distribution systems due to lightning or overvoltage, often an arc discharge occurs at the fault point. The arc discharge, which is caused by a fault current, has a high current, high temperature, strong light emission, etc., thus it sometimes causes heavy damages to electric power equipment. The arc discharge is influenced by the conditions around the arcs, i.e., gas, insulation materials, gap length, weather, etc. Also, the arc voltage along the arc column indicates the characteristics of the arc. If the voltage waveforms of the arcs caused by the fault on transmission or distribution systems are classified, it is possible to find the location and the equipment where the fault occurred. In this paper, the arc voltage data in 6-kV class XLPE cables and 6-kV class overhead lines are analyzed and an artificial neural network method is applied to classify the arc voltage waveforms. The results obtained from the six artificial neural networks developed show that the artificial neural network method is effective for classification of arc voltage waveforms if adequate input parameters are selected.     

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。     

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.     

基于BP网络的保护渣性能预测模型的研究

结合BP网络计算机实验研究 ,建立了保护渣化学组成与性能的预测模型 ,并利用化学组成与性能的关系 ,对网络的实用性进行了检验 ,结果符合保护渣化学组成与性能的关系。保护渣粘度随着保护渣碱度的增大而减小 ,而半球点温度随碱度增大而增大 ;保护渣的半球点温度和粘度都随着渣中CaF2 含量的增加而减小。用BP网络的误差反向传播算法建立的保护渣的化学组成与性能的预测模型 ,得出的预测值与实际值的误差小 ,对保护渣的设计与应用都有一定的指导作用。     

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.     

Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.