自组织网络中的分布式能量感知拓扑控制算法

为了有效利用无线自组织网络的能量,将最小连通支配集和计算几何学相结合,提出了一种自组织网络中的分布式能量感知拓扑控制算法.首先选举能量寿命较长的节点构建能量感知最小连通支配集,保证支配节点有充足的能量完成路由转发任务.之后在能量感知最小连通支配集上面构建Delaunay三角剖分,降低节点数据发送能耗,保证链路的能量有效性和网络拓扑的平面性.在原有连通支配集失效的情况下,选举能量寿命较长的替代节点进行拓扑重构,实现动态网络能量均衡.仿真结果表明,本算法在获得小的支配集的同时延长了网络寿命.      

冗余直接驱动阀系统的余度控制策略

针对冗余直接驱动阀伺服系统中由于余度降级所造成的性能降低问题,提出一种神经网络自适应滑模余度控制策略.利用径向基函数神经网络RBFNN(Radial Basis Function Neural Network) 的在线学习功能,对系统发生的变化进行快速自适应补偿,使系统状态趋近于滑模面,提高跟踪精度和鲁棒性;并通过与比例微分PD(Proportional-Derivative)算法的并行控制,促进RBFNN的收敛,增强系统的稳定性.通过与PID(Proportional-Integral-Derivative)切换控制策略的对比研究,表明RBFNN自适应滑模余度控制方法不但设计简单,而且能够有效克服余度降级带来的系统性能下降的问题,极大地改善了系统的品质.      

计算机网络防御策略模型

目前计算机网络防御研究中缺乏高层且易于细化的策略建模方法,因此在分析Or-BAC模型(Organization Based Access Control model)的基础上,对网络防御控制行为进行抽象,建立计算机网络防御策略模型(CNDPM,Computer Network Defense Policy Model).该模型对保护、检测和响应等策略进行统一建模,并引入角色、视图、活动自动分配的方法,以提高分配的效率,同时给出了策略到规则的推导规则,以细化为具体的防御规则.还给出了策略的完备性、有效性和一致性的形式化描述及分析.实例分析表明,该模型表示的计算机网络防御策略,能够有效地转化为防御规则,具有较好的实用性和扩展性.      

CAN总线在电液伺服阀性能测试系统中的应用

伺服阀性能测试系统中,测控电路和计算机间的模拟信号传输易受外界干扰,而把信号以数码形式通过CAN总线传输的方法,可以彻底消除外界干扰问题,还能提高系统的性能和可靠性。介绍了CAN总线硬件电路、程序设计及CANopen通讯协议的实现,讨论了CAN总线在测试系统上的实际应用。     

Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.     

Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field

The paper deals with the relation of the southern orientation of the north–south component _Bz$B_z$ of the interplanetary magnetic field to geomagnetic activity (GA) and subsequently a method is suggested of using the found facts to forecast potentially dangerous high GA. We have found that on a day with very high GA hourly averages of _Bz$B_z$ with a negative sign occur at least 16 times in typical cases. Since it is very difficult to estimate the orientation of _Bz$B_z$ in the immediate vicinity of the Earth one day or even a few days in advance, we have suggested using a neural-network model, which assumes the worse of the possibilities to forecast the danger of high GA – the dominant southern orientation of the interplanetary magnetic field. The input quantities of the proposed model were information about X-ray flares, type II and IV radio bursts as well as information about coronal mass ejections (CME). In comparing the GA forecasts with observations, we obtain values of the Hanssen–Kuiper skill score ranging from 0.463 to 0.727, which are usual values for similar forecasts of space weather. The proposed model provides forecasts of potentially dangerous high geomagnetic activity should the interplanetary CME (ICME), the originator of geomagnetic storms, hit the Earth under the most unfavorable configuration of cosmic magnetic fields. We cannot know in advance whether the unfavorable configuration is going to occur or not; we just know that it will occur with the probability of 31%.     

基于类脑模型与深度神经网络的目标检测与跟踪技术研究

目标检测与跟踪技术广泛应用于交通、医疗、安保和航天等领域.目前,目标检测与跟踪技术面临目标微弱、背景复杂、目标被遮挡等挑战.同时,随着脑科学研究的不断深入,人们对人脑视觉系统的理解逐渐透彻,利用类脑计算解决复杂背景下高精度目标检测与跟踪问题成为相关领域的重要研究方向.本文结合神经工程导向的类脑模型和计算机工程导向的深度神经网络(Deep Neural Networks, DNNs),提出多种基于类脑模型与深度神经网络的目标检测与跟踪算法,包括：基于演算侧抑制的目标检测算法,基于结构 对比度(Structure Contrast, SC)视觉注意模型的弱小目标检测算法和基于记忆机制与分层卷积特征的目标跟踪算法.实验结果表明,将类脑模型和深度神经网络应用于目标检测和跟踪领域,有利于实现复杂条件下的高精度目标检测和鲁棒性目标跟踪.     

基于自适应迭代学习的小行星绕飞容错控制

对于小行星绕飞任务的探测器姿态控制问题,已有方法大都考虑了干扰力矩和参数不确定等因素,而忽视了执行器故障情况。针对执行器故障条件下的小行星探测器姿态控制问题,提出了一种基于自适应迭代学习的容错控制方法。所设计的控制器包括两部分：其一针对执行器故障,设计了自适应迭代学习控制器,采用类滑模的思想和自适应迭代学习算法对控制器参数进行调整,进而补偿执行器故障带来的影响,保证系统在控制输出不足情况下的高精度姿态稳定性;其二针对探测器参量变化、外部环境干扰等不确定情况,设计了基于自适应神经网络的迭代学习控制器,采用径向基函数（RadialBasisFunction,RBF）神经网络对系统非线性部分进行逼近,同时对控制器参数进行自适应迭代学习调整,进而保证系统在不确定情况下的动态性能。数值仿真结果表明该控制器能够有效抑制外部环境干扰和内部参数变化带来的不利影响,在执行器部分失效甚至完全失效故障情况下,仍能保证系统的鲁棒性并实现误差在10^-2数量级内的较高姿态控制精度。     

分布式对象技术在电信网管系统中的应用

随着电信网络的迅速发展,开放、高效、可扩展的电信网管系统成为当前电信网络管理的迫切需求.分布式网络管理成为电信网络管理的主要发展方向.分布式对象技术是对象技术在分布式领域的发展分支,对系统结构开放、分布式处理及软件复用提供了较好的支持.基于分布式对象技术设计的分布式电信网管系统适应当前网管需求,能够解决目前网络系统所面临的问题,具有较大应用价值.