一种无线链状网络路由算法的研究与应用

文章基于货运列车状态监控的无线传感器网络应用,针对网络节点能量有限以及链状网络节点负载不均衡的问题进行分析和研究,提出一种适合该应用的新型路由算法,该算法基于网络节点可通过功率控制来调整通信距离这一前提,为每个传感器节点选择合适的数据传输路径,以达到整个网络节省能耗,负载均衡的目的。仿真结果验证了该算法有效地平衡了网络负载,节省了网络能量,提高了网络生命周期。     

BP神经网络在波音737自动刹车故障诊断中的应用

随着人工神经网络研究的兴起,智能故障诊断技术成为近年来人们研究的一个热点问题。自动刹车是飞机起降时的重要装置,需要定期对其进行故障检测,以免形成飞行安全隐患,是飞机故障诊断的一个重要内容。本文从传统的BP神经网络算法出发,通过算法改进建立了波音737自动刹车故障诊断的神经网络模型,通过提取故障样本对网络进行训练和诊断,仿真结果显示这种方法能较理想地对波音737飞机自动刹车系统的故障进行分析和检测,对于建立波音飞机的故障自动诊断系统具有重要意义。     

基于危险理论的人工免疫原理与应用*

侧重以危险理论的基本机制为线索,对危险理论的研究现状加以系统论述.首先以危险理论的免疫模型入手,归纳提炼出仿真机理并建立起隐喻算法;介绍其中具有代表性领域中的应用情况;最后对基于危险理论的人工免疫研究的下一步工作进行展望.     

人工神经网络在组织网络化发展评价中的应用*

通过对给定样本模式的学习,构建的三层神经网络模型能够获取评价专家的经验、知识、主观判断以及对目标重要性的权重协调能力,较好地保证评价结果的客观性.实例证明,人工神经网络方法应用于组织网络化发展评价是可行和有效的.     

Synchronization in complex networks of phase oscillators: A survey

The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in real-world synchronization phenomena, that generalize the celebrated Kuramoto model, and that feature a rich phenomenology. We review the history and the countless applications of this model throughout science and engineering. We justify the importance of the widespread coupled oscillator model as a locally canonical model and describe some selected applications relevant to control scientists, including vehicle coordination, electric power networks, and clock synchronization. We introduce the reader to several synchronization notions and performance estimates. We propose analysis approaches to phase and frequency synchronization, phase balancing, pattern formation, and partial synchronization. We present the sharpest known results about synchronization in networks of homogeneous and heterogeneous oscillators, with complete or sparse interconnection topologies, and in finite-dimensional and infinite-dimensional settings. We conclude by summarizing the limitations of existing analysis methods and by highlighting some directions for future research.     

Variance analysis of identified linear MISO models having spatially correlated inputs,with application to parallel Hammerstein models

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.     

Output controllability and optimal output control of state-dependent switched Boolean control networks

In the present paper, we investigate the output-controllability and optimal output control problems of a state-dependent switched Boolean control network. By using the semi-tensor product, the algebraic form of the system is obtained. Then, output-controllability problems of the system are discussed and some necessary and sufficient conditions are given. Next, the Mayer-type optimal output control issue is considered and an algorithm is provided to find out the control sequence. At last, an example is given to show the effectiveness of the main results.     

Local artificial boundary conditions for Schrödinger and heat equations by using high-order azimuth derivatives on circular artificial boundary

The aim of the paper is to design high-order artificial boundary conditions for the Schrödinger equation on unbounded domains in parallel with a treatment of the heat equation. We first introduce a circular artificial boundary to divide the unbounded definition domain into a bounded computational domain and an unbounded exterior domain. On the exterior domain, the Laplace transformation in time and Fourier series in space are applied to achieve the relation of special functions. Then the rational functions are used to approximate the relation of the special functions. Applying the inverse Laplace transformation to a series of simple rational function, we finally obtain the corresponding high-order artificial boundary conditions, where a sequence of auxiliary variables are utilized to avoid the high-order derivatives in respect to time and space. Furthermore, the finite difference method is formulated to discretize the reduced initial–boundary value problem with high-order artificial boundary conditions on a bounded computational domain. Numerical experiments are presented to illustrate the performance of our method.     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

结合矩阵分解的混合型社会化推荐算法

推荐系统是用来解决当今时代信息过载的重要工具。随着在线社交网络的出现和普及,一些基于网络推荐算法研究的出现,已经引起研究者的广泛关注。信任是社会网络中的重要信息之一,通常用来改进基于社交网络的推荐系统,然而,大多数信任感知的推荐系统忽略了用户有不同行为偏好在不同的兴趣域;本文不仅考虑了用户间特定域信任网络,并且结合推荐项目之间特征属性信息,提出了一种新型社会化推荐算法(H-PMF)。实验表明,H-PMF算法在评分误差和推荐精度上都取得了更好的效果。