Perturbation analysis and condition numbers of symmetric algebraic Riccati equations

This paper is devoted to the perturbation analysis of symmetric algebraic Riccati equations. Based on our perturbation analysis, the upper bounds for the normwise, mixed and componentwise condition numbers are presented. The results are demonstrated by our preliminary numerical experiments.     

Artificial neural networks and genetic algorithm for bearing fault detection

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring

As the nuclear power plants within the UK age, there is an increased requirement for condition monitoring to ensure that the plants are still be able to operate safely. This paper describes the novel application of Intelligent Systems (IS) techniques to provide decision support to the condition monitoring of Nuclear Power Plant (NPP) reactor cores within the UK. The resulting system, BETA (British Energy Trace Analysis) is deployed within the UK’s nuclear operator and provides automated decision support for the analysis of refuelling data, a lead indicator of the health of AGR (Advanced Gas-cooled Reactor) nuclear power plant cores. The key contribution of this work is the improvement of existing manual, labour-intensive analysis through the application of IS techniques to provide decision support to NPP reactor core condition monitoring. This enables an existing source of condition monitoring data to be analysed in a rapid and repeatable manner, providing additional information relating to core health on a more regular basis than routine inspection data allows. The application of IS techniques addresses two issues with the existing manual interpretation of the data, namely the limited availability of expertise and the variability of assessment between different experts. Decision support is provided by four applications of intelligent systems techniques. Two instances of a rule-based expert system are deployed, the first to automatically identify key features within the refuelling data and the second to classify specific types of anomaly. Clustering techniques are applied to support the definition of benchmark behaviour, which is used to detect the presence of anomalies within the refuelling data. Finally data mining techniques are used to track the evolution of the normal benchmark behaviour over time. This results in a system that not only provides support for analysing new refuelling events but also provides the platform to allow future events to be analysed. The BETA system has been deployed within the nuclear operator in the UK and is used at both the engineering offices and on station to support the analysis of refuelling events from two AGR stations, with a view to expanding it to the rest of the fleet in the near future.     

基于深层条件随机场的生物医学命名实体识别*

生物医学命名实体识别是从生物医学文献中获取关键知识的基础与关键任务.文中提出基于深层条件随机场的生物医学命名实体识别方法,构建多层结构的深层条件随机场模型,在不同层次的特征上结合增量式学习策略,选择最优特征集.最后通过基于〈全名,缩写〉对和基于领域信息的错误纠正算法,进一步修正识别结果.在生物医学命名实体评测语料JNLPBA上的实验验证文中方法的有效性.     

基于效率函数求解单件车间调度问题算法的完善

单件小批量生产形式下的单件车间（Job-shop)调度是生产计划中的一个重要问题，本文在文献[1]提出的求解Job-shop调度问题的动排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率，并在VBA For Projcet2000中实现了该算法，经分析及实验验证，利用这种算法求解Job-shop调度问题，可得到十分满意的结果。     

基于效率函数求解单件车间调度问题算法的软件实现和实例分析

单件小批量生产形式下的单件车间（Job-shop）调度是生产计划中的一个重要问题。西方在文献[1]提出的求解Job-shop调度问题的初排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率；在软件实现中引入了“虚工序”的概念，并在“VBA ForProject2000中实现了该算法。经分析及实验验证，利用这种算法求解Job-shop调度问题可得到十分满意的结果。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

基于状态监测的全数字预测倍频技术研究

随着信息技术的迅速发展,在电力、石化等众多行业里得到广泛的重视和应用的旋转机械状态监测技术面临着巨大的发展和挑战。对振动信号的采集与分析是设备状态监测和故障诊断的基础,本文在对传统数字倍频器的分析基础上,结合现场实际情况设计出一种带预测功能的全数字倍频器,该项技术在状态监测系统中得到了很好的应用。     

Excel的应用小技巧

现代计算机的使用越来越普及,办公自动化也随之热门起来,而Office系列办公软件也就首当其充的成了办公的常用工具。并且对于办公软件的应用要求也越来越高,工作也越来越细,而Office套餐中的Excel在实际应用中却隐藏着不少的小技巧。     

State observability and condition observability for a class of interacting discrete event systems

This paper introduces the concepts of state observability and condition observability for condition systems, a class of systems composed of discrete state components which interact via discrete binary signals called conditions. Given a set of externally observed conditions, state observability implies that the state of the system can be determined from the observations, and condition observability implies that all unobserved input and output conditions of the system can be determined from the observations. In this paper, we present a class of systems which is state observable and condition observable. We present a method to synthesize an observer system to provide state and condition signal estimates for a single component subsystem.