民用航空器人为差错分析与航空安全

安全是民航永恒的主题,随着科技水平的不断提升,我国民航事业得到了快速的发展,民用航空器的可靠性也得到了明显的提升。但同时,随着民用航空器可靠性的提升,人为差错在航空维修事故中所占的比例却逐年提高。民用航空器的维修和安全管理与民航安全有着密切的联系,因此,当前要切实提高民用航空器的维修质量,就必须对航空器维修中的人为因素进行分析,得出其产生的原因,从而制定有效策略来避免问题的产生,保证航空安全。     

专家系统中基于Jess的推理研究

介绍了专家系统开发平台Jess,并把Jess运用到民航机务维修差错预警专家系统中进行研究.该系统中维修差错以规则形式表示,推理机使用基于规则的不确定性推理方法进行推理,使系统达到有效预测和控制可能发生的维修差错,从而保证飞行安全.     

基于MAS的航空机务维修差错预警专家系统

机务维修是关系到民用航空安全和效益的重要因素之一,是飞行安全的基础,因此迫切需要建立航空机务维修差错预警专家系统。采用Agent技术作为低层支撑技术,将基于规则的不确定性推理和基于案例推理的预警方法引入航空机务维修差错预警领域,给出了总体结构的设计以及关键技术的实现,从而为航空机务维修差错预警提供了一种新的思路和方法。     

大数据格局下信息技术辅助机场安全管理

随着我国民航运输量增长,机场成为重要的旅客集散中心,支持机场运转的业务系统全天候的发挥着重要作用,一个千万级机场年运行数据规模达拍字节1级别.以技术应用创新挖掘机场大数据价值,在安检、公安、空保等安全部门运用智能视频分析、机器学习辅助、人脸识别和移动应用等技术,能推动机场安全管理模式创新,弥补人为疏漏,提高机场安全防范和保障能力.     

一种以人为中心的风险评估算法

从人为差错的辨识、概率计算和后果量化三个方面讨论人因事件风险评估的流程,并针对这三个关键问题设计相关的解决方案。针对人为差错辨识问题,设计一种统一的人为差错基本分类框架,作为差错辨识过程的模板库;针对人为差错概率计算问题,提出首先计算人为差错总体概率,然后结合历史事故统计资料计算具体差错模式发生概率的新方法;针对后果量化问题,按照先定性后定量的原则,设计一种后果量化值的确定方法。     

变频器控制的电气设备常见故障及方法对策

人需要保养方能健康长寿,设备需要维修方能经久耐用.变频器控制的电气设备故障的维修是机床设备安全生产、延长使用寿命的重要保障.一般来讲,电气设备故障论其原因,大致有两种情况:一是自然故障;二是人为故障.不管是自然故障还是人为故障,只要是出了故障就应该及时进行检修和维护,本文就变频器控制的电气设备故障的常见干扰故障及对策进行一些简单探讨和分析.     

基于MySQL的飞机维修管理系统的设计

随着人们对民航的需求越来越大,民航的安全稳定性成为人们高度关切的问题.通过对飞机维修管理系统的核心功能和业务需求的研究,确定该系统采用B/S模式Web应用程序,基于J2EE体系结构来实现,结合XML技术.系统部署服务器操作系统为Windows Server 2008,Web发布中间件为TOMCAT 8.5,数据库采用M...     

基于本体的空管人为差错分析研究与应用

为实现空管人为差错致因及危险等级语义分析,开发人为差错本体分析软件。通过空管运行人为差错(HeraJanus)及人为差错预测(Hera-Predict)手册中获取的领域知识构建领域本体;结合事故调查报告知识创建存储本体,建立Hera-Janus本体知识库,根据类与个体之间的关系定义Jena推理规则。通过Eclipse平台中的Jena应用调用Pellet推理机分析实际案例,在人机交互界面给出差错类型及危险程度,其结果表明了空管事故智能化分析的有效性和可行性。     

用汇编语言实现BCH解码校验算法

介绍数据传输中BCH解码校验用汇编语言实现的算法.算法包含BCH码的差错检验、差错位查找和差错纠正,同时列出相关主要子程序清单并予说明.     

关于直升机维修差错控制及预防措施研究

直升机维修差错是航空事故的主要诱发因素，占到世界航空事故总数的85%，使航空交通的发展受到严重影响。对直升机维修差错的本质进行了详细的研究和分析，并进一步提出直升机维修中差错控制和预防的相关措施和对策。     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

SEO技术研究

为了利用搜索引擎优化SEO（Search Engine Optimization）技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。