Adaptive Fault Monitoring in All-Optical Networks Utilizing Real-Time Data Traffic

We propose a novel fault detection and localization scheme for all-optical networks with the information of real-time data traffic. Our adaptive fault localization framework is based on combining passive and proactive monitoring solutions, together with adaptive management in two phases. Numerical results indicate that our proposed scheme has good scalability, in terms of the number of fault monitors required. Also, we show that our framework allows more flexible network design, and requires much less monitoring bandwidth when compared with the passive monitoring solutions.     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

A novel range-free localization based on regulated neighborhood distance for wireless ad hoc and sensor networks

Range-free localization methods are suitable for large scale wireless ad hoc and sensor networks due to their less-demanding hardware requirements. Many existing connectivity- or hop-count-based range-free localization methods suffer from the hop-distance ambiguity problem where a node has a same distance estimation to all of its one-hop neighbors. In this paper, we define a new measure, called regulated neighborhood distance (RND), to address this problem by relating the proximity of two neighbors to their neighbor partitions. Furthermore, we propose a new RND-based range-free localization method, and compare our localization algorithm with peer classical algorithms in different network scenarios, which include grid deployment, random uniform deployment, non-uniform deployment and uniform deployment with a coverage hole. Simulation results show that ours can achieve better and reliable localization accuracy in these network scenarios.     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.     

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters/states cannot be provided or determined accurately. These values can be related to the geometry (wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi-Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the first case considers robust multi-objective (single-disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero-structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.     

The calcium-phosphorus and the simultaneous calcium-oxygen and calcium-sulfur equilibria in liquid iron

The equilibrium Ca₃P_2(s) = 3[Ca] + 2[P] was studied at 1600 ° by equilibrating liquid iron, saturated with Ca₃P₂, and contained in a TiN crucible, with Ca vapor. The source of Ca was liquid Ca contained in an Mo crucible, and the vapor pressure of Ca was varied by varying the position of the Mo crucible in the temperature gradient of a vertical tube furnace. A least-squares analysis of the data gave and. The simultaneous equilibria CaO_(s) = [Ca] + [O] and CaS_(s) = [Ca] + [S] were studied at 1600 ° by equilibrating liquid iron, contained in a pressed and sintered CaO-CaS crucible with Ca vapor. The advantage of this technique is that two equilibrium constants,K _cas andK _cao, and two interaction coefficients, and can be determined from one set of experiments. It was determined that, at 1600 °,K _cas = 5.9 × 10⁻⁸ K _cao = 5.5 × 10⁻⁹,, and. Formerly Graduate Students     

港口机车故障监测与诊断系统研究

针对港口机车运行现状,介绍了一种基于Visual Basic语言和Microsoft SQL Server2000数据库的机车故障监测与诊断系统的研究。该系统通过传感器采集数据信息,经预处理后,通过无线网络将整合数据传送给计算机,最后利用知识库对数据进行处理分析以及显示。该系统具有设计简单、操作方便、界面友好及可靠性好等特点。     

基于五层十五级遥感数据结构的并行算法研究

针对当前环境与监测预报小卫星宽覆盖和海量数据显示与处理不足的现状,设计五层十五级遥感数据组织结构,并依据此结构设计一种新的并行计算算法,得到并行计算的最大近优估算值,优化环境与监测预报小卫星海量数据的遥感产品生产能力,提高环境监测预报效率。     

基于LabVIEW的医学影像学教学系统的设计

医学影像学一直是各医学院校教学的重点课程之一。本文通过基于LabVIEW软件制作成医学影像学肝门静脉图像教学系统供教学使用,并为临床诊断提供参考,实践证明取得了良好的效果。     

数据挖掘技术在高校就业预测分析中的应用

针对当前高校面临的就业问题,本文利用决策树C4.5算法对某高校近三年的毕业生就业信息进行分析,建立就业分析预测模型。首先选取高校就业信息管理系统中的就业信息,对信息进行采集和预处理,然后利用分类算法中的C4.5分类算法进行挖掘,最后对建立的决策树进行规则提取,从而得到毕业生就业预测规则。实验结果表明建立的就业分析模型是有效的、可靠的,对于全面提高大学生的就业能力、完善就业服务体系等方面具有一定指导意义。