Model checking response times in Networked Automation Systems using jitter bounds

Response time (RT) of Networked Automation Systems (NAS) is affected by timing imperfections induced due to the network, computing and hardware components. Guaranteeing RT in the presence of such timing imperfections is essential for building dependable NAS, and to avoid costly upgrades after deployment in industries.This investigation proposes a methodology and work-flow that combines modelling, simulation, verification, experiments, and software tools to verify the RT of the NAS during the design, rather than after deployment. The RT evaluation work-flow has three phases: model building, modelling and verification. During the model building phase component reaction times are specified and their timing performance is measured by combining experiments with simulation. During the modelling phase, component based mathematical models that capture the network architecture and inter-connection are proposed. Composition of the component models gives the NAS model required for studying the RT performance on system level. Finally, in the verification step, the NAS formal models are abstracted as UPPAAL timed automata with their timing interfaces. To model timing interfaces, the action patterns, and their timing wrapper are proposed. The formal model of high level of abstraction is used to verify the total response time of the NAS where the reactions to be verified are specified using a subset of timed computation tree logic (TCTL) in UPPAAL model checker. The proposed approach is illustrated on an industrial steam boiler deployment.     

基于高能效及公平性的移动设备定位算法

通过分析校园师生基于GPS的真实活动轨迹,以及何种算法才最适合具有自私性的移动用户展开合作,提出一种高能效及用户公平定位技术.通过采用代理设备合作思想,提出一种集中式最优算法,在保证固定精度界限和设备公平的基础上,实现移动设备GPS时间平均功率最小化.通过控制退避时间和GPS关闭时间,提出一种分布式准最优算法.最后,基于多种拓扑结构进行全面的仿真实验,验证了算法的高能效性和公平性.     

计算机编程语言与输入设备的发展问题初探

现代化建设事业的发展离不开计算机水平的提高。计算机编程语言作为人与机器沟通的桥梁,在计算机系统里有着非常重要的地位。本文从探讨计算机编程语言的概念出发,详细阐述了计算机编程语言所包含的内容和基本分类。接着笔者又深入分析了计算机输入设备的发展问题。最后,针对计算机编程语言和输入之间的联系问题,笔者做了观点性和理论性的论述分析。     

基于PCA-Logistic模型的围岩稳定性分类研究

为了建立易于理解和应用的围岩稳定性分类模型,采用主成分分析法对围岩样本数据进行降维处理,以Logistic模型来描述围岩稳定性影响因素与围岩稳定性之间的经验关系。选取影响围岩分类的5个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到100个标准样本,用于建立PCA-Logistic模型。分析结果显示,PCA-Logistic模型拟合效果良好,对工程实例的预测结果同投影寻踪模型和分形-插值模型完全一致,表明该模型在围岩稳定性评价中具有可行性和有效性。     

计算机内存的优化及管理浅析

信息总量、网络浏览人数以及人们对网络享受需求的增加,促使着信息制作水平的提高,制作水平的提高在给人们带来了更好的视觉和听觉享受的同时,也对计算机的硬件系统提出了巨大的考验.计算机内存作为计算机硬件系统的重要组成部分,能否对内存做好合理的划分,对计算机的体验具有重要的意义.     

Bounded Consensus Algorithms for Multi‐Agent Systems in Directed Networks

In this paper, the consensus problem is investigated via bounded controls for the multi‐agent systems with or without communication. Based on the nested saturation method, the saturated control laws are designed to solve the consensus problem. Under the designed saturated control laws, the transient performance of the closed‐loop system can be improved by tuning the saturation level. First of all, asymptotical consensus algorithms with bounded control inputs are proposed for the multi‐agent systems with or without communication delays. Under these consensus algorithms, the states’ consensus can be achieved asymptotically. Then, based on a kind of novel nonlinear saturation functions, bounded finite‐time consensus algorithms are further developed. It is shown that the states’ consensus can be achieved in finite time. Finally, two examples are given to verify the efficiency of the proposed methods.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

基于概念的形状分类与识别

基于内容的图像检索已经成为图像检索的一个重要研究方向。其中利用形状特征可以实现形状较为明显的各类图像库的检索。该文对图像形状特征的提取和描述方法进行了较深入地研究,选取其中一部分特征值,分别利用BP神经网络和支持向量机进行形状识别,取得令人满意的实验效果。     

时间序列季节调整方法在中国的发展:PBC版X-12-ARIMA

中国人民银行(PBc)版X-12-ARIMA软件是基于中国特点而定制的时间序列季节调整软件.通过总结时间序列季节调整方法的特点以及相应软件在国外的发展,针对我国应用的特点,尤其是春节因素的考虑,在解剖X-12-ARIMA方法原理的基础上,在春节因素计算方法、软件应用界面以及用户使用帮助等3个主要方面加以改进,具有数据导入、调整设置文件、运行方式以及结果输出4方面的特色.     

Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.