基于无线通信技术的FFU风机群控系统

针对大型洁净工程实现自动化控制的需要,利用数字无线通信技术,设计了一种新型网络化的多风机过滤器单元(FFU)群控系统,详细介绍了系统的组成实现原理和软硬件设计方法;系统间采用基于射频收发芯片nRF2401实现数据的无线收发,有效地减少了系统布线和设计成本,提高了系统的组网灵活性和适用性;对设计开发的系统进行了测试实验,实验结果表明该系统数据通信正确可靠,系统具有结构简单、组网灵活、监控效果好和可扩展性强等优点。     

攻击型无人机切换控制系统容错算法研究

攻击型无人机单侧发射或投放重型武器时,可将飞行控制系统看作是一个切换控制系统。针对常见的常值型和时变型两种执行器故障,提出了一种自适应观测器设计方法,同时结合状态反馈控制设计了容错控制器。通过Lyapunov函数推出了观测器的误差是关于故障界输入状态实际稳定的充分条件,通过平均驻留时间方法证明了切换控制系统是全局输入状态实际稳定的。某型攻击型无人机仿真实例表明,此方法能在准确估计故障大小的基础上,有效保持切换飞行控制系统的性能,应用前景十分广阔。     

在CodeWarrior编译环境下运行μC／OS—Ⅲ

μC／OS-Ⅱ是面向8／16位及低端32位单片机应用的RTOS,而新近推出的μC／OS-Ⅲ则面向高性能32位单片机,如ARMCortex等。以Cortex—M4为内核的Kinetis系列单片机,不仅用于全国大学生飞思卡尔杯智能车竞赛,也用于诸多大学的嵌入式系统教学,官方提供的开发环境是CodeWarrior。本文介绍如何利用CodeWarrior开发环境,将μC／OS-Ⅲ在Kinetis单片机上运行起来,以便将μC／OS-Ⅲ引入教学、科研与应用。     

Robust ℋ︁∞ PID control for multivariable networked control systems with disturbance/noise attenuation

In this paper, we study the design problem of PID controllers for networked control systems (NCSs) with polyhedral uncertainties. The load disturbance and measurement noise are both taken into account in the modeling to better reflect the practical scenario. By using a novel technique, the design problem of PID controllers is converted into a design problem of output feedback controllers. Our goal of this paper is two‐fold: (1) To design the robust PID tracking controllers for practical models; (2) To develop the robust ??_∞ PID control such that load and reference disturbances can be attenuated with a prescribed level. Sufficient conditions are derived by employing advanced techniques for achieving delay dependence. The proposed controller can be readily designed based on iterative suboptimal algorithms. Finally, four examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Mining blackhole and volcano patterns in directed graphs: a general approach

Given a directed graph, the problem of blackhole mining is to identify groups of nodes, called blackhole patterns, in a way such that the average in-weight of this group is significantly larger than the average out-weight of the same group. The problem of finding volcano patterns is a dual problem of mining blackhole patterns. Therefore, we focus on discovering the blackhole patterns. Indeed, in this article, we develop a generalized blackhole mining framework. Specifically, we first design two pruning schemes for reducing the computational cost by reducing both the number of candidate patterns and the average computation cost for each candidate pattern. The first pruning scheme is to exploit the concept of combination dominance to reduce the exponential growth search space. Based on this pruning approach, we develop the gBlackhole algorithm. Instead, the second pruning scheme is an approximate approach, named approxBlackhole, which can strike a balance between the efficiency and the completeness of blackhole mining. Finally, experimental results on real-world data show that the performance of approxBlackhole can be several orders of magnitude faster than gBlackhole, and both of them have huge computational advantages over the brute-force approach. Also, we show that the blackhole mining algorithm can be used to capture some suspicious financial fraud patterns.     

Financial distress prediction using support vector machines: Ensemble vs. individual

Financial distress prediction (FDP) is of great importance to both inner and outside parts of companies. Though lots of literatures have given comprehensive analysis on single classifier FDP method, ensemble method for FDP just emerged in recent years and needs to be further studied. Support vector machine (SVM) shows promising performance in FDP when compared with other single classifier methods. The contribution of this paper is to propose a new FDP method based on SVM ensemble, whose candidate single classifiers are trained by SVM algorithms with different kernel functions on different feature subsets of one initial dataset. SVM kernels such as linear, polynomial, RBF and sigmoid, and the filter feature selection/extraction methods of stepwise multi discriminant analysis (MDA), stepwise logistic regression (logit), and principal component analysis (PCA) are applied. The algorithm for selecting SVM ensemble's base classifiers from candidate ones is designed by considering both individual performance and diversity analysis. Weighted majority voting based on base classifiers’ cross validation accuracy on training dataset is used as the combination mechanism. Experimental results indicate that SVM ensemble is significantly superior to individual SVM classifier when the number of base classifiers in SVM ensemble is properly set. Besides, it also shows that RBF SVM based on features selected by stepwise MDA is a good choice for FDP when individual SVM classifier is applied.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

VB程序在铁路货车制造行业中小设备中的应用

介绍利用VB语言在铁路货车制造行业中小设备中的软件开发方案的实现,以及对铁路货车制造行业相关专业性要求适应的实现。     

针对肌肉功能障碍患者的数据库研究与开发

建立标准数据库是临床研究病情变化的重要手段,建立适用于肌肉功能障碍患者的标准数据库模板更具有重要临床意义。本研究针对临床上所采集的具有肌肉功能障碍患者的肌力、肌电数据,通过ODBC连接方式,引入了统一建模语言(UML),使用VC++开发工具,设计和完成了Access数据库,以对获取到的大量的肌力、肌电数据进行管理和使用。数据库有效存储并管理肌力、肌电数据,同时能减少数据占有空间和数据冗余,它更好地满足了医院康复科的需求。