PROPAGATION OF GASLESS COMBUSTION FRONTS: NONZERO REACTION RATE IN THE WHOLE DOMAIN

This paper deals with the gasless combustion problem in an infinite one-dimensional medium. The possibility of the existence of steadily propagating reaction waves is stated for system models with nonzero reaction rates everywhere but at the boundaries. Two forms for the chemical reaction rate dependence on the temperature are considered: the classical Arrhenius, and a modified Arrhenius incorporating and ignition temperature. The equations for the steadily propagating waves are studied in the phase-plane of the temperature and its derivative. The analysis first addresses the question of whether steadily propagating waves are admissible. Then bounds for the propagation velocity are sought and found. The results of the closed-form analysis are successfully tested against numerical experiments. Non-steady propagation regimes are also found, and regions in the parameter space associated with different asymptotic dynamical behaviour are identified.     

孤东油田的海油陆采进展

孤东油田极浅海区域新发现几个边际油藏。其中垦东401块水深-2m,距陆地2.7km,为稀油、疏松砂岩油藏,试油获14～25t/d工业油流。在三维地震、测井约束地震反演、数值模拟和油藏经营管理评价基础上编制了产能建设方案,通过修筑海公路、人工岛、丛式井组、大位移水平井、采用数据采集与控制的远程测控和紧急关断技术,能够无人职守、遥测遥控,整体实现了海油陆采。有效动用地质储量233×104t,钻井12口,建成年产油4×104t能力,累产油8.6×104t。目前在垦东12人工岛上钻打了26口井,推广了海油陆采经验。     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

低空大气对连续波干涉仪系统相位稳定性的影响

针对三站连续波干涉仪系统，根据实时测得的系统场区大气折射率漂移数据，应用几何光学方法，从理论上估计了连续波干波仪系统两条微波基线传输的相位稳定性。     

一种基于数据竞争的高分辨率图像的聚类分割算法

近些年,机器人技术得到了迅猛的发展,应用越来越广泛.随着机器人技术的推广和普及,对机器人使用的要求也越来越高,其中对智能机器人的要求尤显迫切.机器视觉是智能机器人研究领域的一个重要研究方向.在机器人视觉系统中,核心问题是目标提取,对目标实时、准确、快速提取的关键技术是图像分割.由于机器人感知的环境的复杂性及目标的多样性,往往导致机器人感知获得的图像数据量较大且图像本身存在不可预知的复杂性,这就对准确的目标分割和提取处理提出了挑战性问题.本文针对高分辨率图像数据集的分割处理,提出一种新的聚类算法,即根据数据点能量和的大小识别类代表点和类成员点,通过数据点间的竞争识别出最有能力成为簇成员的数据点,并将其与mean shift聚类算法有效地结合应用于彩色图像分割问题中,能够快速高效地实现高分辨率图像的目标分割,并得到较好的图像分割效果.实验结果表明,本文算法在分割效果和分割效率上明显优于传统聚类算法.     

双轴旋转惯导系统误差传播特性分析

针对双轴旋转惯导系统的误差传播特性问题,推导出双轴旋转惯导系统的误差方程,通过特征值判据证明了误差方程在李雅普诺夫意义下的稳定性,设计了一种合理的十六次序转位方案,建立了研究误差传播特性的方法,分别仿真了初始对准误差角、陀螺常值漂移、陀螺安装误差、陀螺标度因数误差4种因素对导航误差传播特性的影响。结果表明,4种误差因素大小均与导航误差大小成线性关系,并且满足叠加性;4种误差因素会引起导航姿态、导航速度、导航纬度的常值误差和振荡误差,但不会引起积累误差;陀螺标度因数误差会引起导航经度误差的积累和发散。最后提出了减小双轴旋转惯导系统导航误差的措施,对设计双轴旋转惯导系统有一定的参考价值。     

改进的蠕虫特征自动提取模型及算法设计

文章提出了一种基于序列比对的蠕虫特征自动提取模型,该模型针对现有蠕虫特征自动提取系统的可疑蠕虫样本流量单来源和粗预处理等问题,提出了对网络边界可疑流量和蜜罐捕获网络流量统一的聚类预处理,并使用改进的T-Coffee多序列比对算法进行蠕虫特征提取。实验分别对Apache-Knacker和TSIG这两种蠕虫病毒进行特征提取,从实验结果可以看出文章提出的模型产生的特征质量优于比较流行的Polygraph、Hamsa两种技术。     

基于Moran过程的竞争性舆情信息的网络传播博弈模型

新的媒体环境下,在享受不同形式在线社交网络提供便捷信息互动渠道的同时,需要加强对信息传播的管理与引导,防止其发展成为负面舆情信息肆意滋生的温床。结合传统演化博弈论与复杂网络理论,提出了竞争性舆情信息的网络传播博弈模型,并理论分析了正面舆情信息传播占优的收益条件及群体规模;随后,结合实际案例,通过仿真实验对比分析三种博弈环境下网络舆情的演化特征。三种博弈环境下,正面舆情信息传播策略占优条件均与收益有关,弱选择情境对收益参数的要求最为严格;强、弱选择情景下策略占优的条件还与群体规模有关,且强选择情景对群体规模的要求更为严格。     

Design of robotic visual servo control based on neural network and genetic algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.