Improved AODV Routing Protocol Based on Link Stability and Channel Switching

Ad hoc on-demand distance vector （AODV） routing is one of the typical reactive routing protocols of vehicular ad hoc networks （V ANET ）. Considering link stability and channel switching can greatly improve the QoS of protocols, in this paper, we propose a novel routing protocol： optimized cross-layer AODV （CL _ AODV） designed for V ANET. It utilizes the frame transmission efficiency （FIE）, path bandwidth in media access control （MAC） layer and signal-to-noise ratio （SNIR） in physical （PHY） layer to improve the link stability. In other words, it can increase packet delivery ratio effectively. In addition, end-to-end delay will be decreased based on the channel switching. According to the simulation, it is shown that the packet delivery ratio of CL_AODV is almost up to 99% and the highest compared to AODV and muti-constrained QoS AODV （MQ _ AODV）. The delay of CL_ AODV is almost half of MQ_AODV＇s and 1/3 of AODV＇s. Meanwhile, CL_AODV has the highest routing overhead or energy consuming. Because of the feature of V ANET, the disadvantage can be ignored.     

A delay-constrained energy-efficient component carrier adjusting scheme for LTE-Advanced systems

The energy efficiency and packet delay tradeoffs in long term evolution-advanced （LTE-A） systems are investigated.Analytical expressions are derived to explain the relation of energy efficiency to mean packet delay,arrival rate and component carrier （CC） configurations,from the theoretical respective which reveals that the energy efficiency of multiple CC systems is closely related to the frequency of CCs and the number of active CCs.Based on the theoretical analysis,a CC adjusting scheme for LTE-A systems is proposed to maximize energy efficiency subject to delay constraint by dynamically altering the on/off state of CCs according to traffic variations.Numerical and simulation results show that for CCs in different frequency bands with equal transmit power,the proposed scheme could significantly improve the energy efficiency of users in all aggregation levels within the constraint of mean packet delay.     

Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis

To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.     

Robust Design for Generalized Minimum Variance Controllers

In order to improve the robustness and noise resistance of generalized minimum valance control systems, several generalized minimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance caused by the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robust design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.     

Performance Improvement of Distributed Systems by Autotuning of the Configuration Parameters

The performance of distributed computing systems is partially dependent on configuration parameters recorded in configuration files. Evolutionary strategies, with their ability to have a global view of the structural information, have been shown to effectively improve performance. However, most of these methods consume too much measurement time. This paper introduces an ordinal optimization based strategy combined with a back propagation neural network for autotuning of the configuration parameters. The strategy was first proposed in the automation community for complex manufacturing system optimization and is customized here for improving distributed system performance. The method is compared with the covariance matrix algorithm. Tests using a real distributed system with three-tier servers show that the strategy reduces the testing time by 40% on average at a reasonable performance cost.     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.     

Testing Cross-Talk Induced Delay Faults in Digital Circuit Based on Transient Current Analysis

The delay fault induced by cross-talk effect is one of the difficult problems in the fault diagnosis of digital circuit. An intelligent fault diagnosis based on IDDT testing and support vector machines （SVM） classifier was proposed in this paper. Firstly, the fault model induced by cross-talk effect and the IDDT testing method were analyzed, and then a delay fault localization method based on SVM was presented. The fault features of the sampled signals were extracted by wavelet packet decomposition and served as input parameters of SVM classifier to classify the different fault types. The simulation results illustrate that the method presented is accurate and effective, reaches a high diagnosis rate above 95%.     

Monitor automatic gauge control strategy with a Smith predictor for steel strip rolling

The simplified transfer function diagram block for a monitor automatic gauge control (Mon-AGC) system of strip steel rolling process was investigated. The new notion of strip sample length was given. In this way, the delay time varying with the rolling speed was evaded. After a Smith predictor was used to monitor the AGC system, the control laws were deduced for both proportional and integral regulators. The control strategies showed that by choosing the controller parameter P=∞ for both control algorithms each regulator could compensate the whole strip gage error in the first control step. The result shows that the integral algorithm is more controllable for the system regulating process and has a better steady-state precision than the proportional regulator. Compared with the traditional control strategy, the new control laws have a faster response speed and a hieher steadv-state precision.     

Carbon Emission Modeling and Analysis in Manufacturing Process for Numerical Control Machine Tools

Reducing carbon emissions（ CEs） is the urgent demand all over the world. In order to realize the low-carbon numerical control（ NC） machining, the evaluation model of a part＇s manufacturing carbon emission with NC machine tools was built by considering the influences of the cutting tool geometrical parameters.The manufacturing CEs were produced by electric power,cutting tools,and cutting fluid consumed in manufacturing process. The parameters of cutting tools affected not only the CEs,but also the machining quality. Then the actual constraint models of the machine performance,machining quality were given in order to optimize the cutting parameters and achieve the low-CEs. Finally,a case was given to analyze the influences of the cutting tool angles on the manufacturing CEs. The results show that the CEs decrease as the rake angle and edge angle increase under the constraints of the machine specifications and machining quality.     

Research and Application of All-Position Automatic Welding Machine

The all-position automatic welding machine system is the special welding system for pipeline girth automatic welding on site, which has been widely used in the long distance pipeline construction projects due to the advantages of automatic control for welding parameters at all position, moving speed of bugs and operating. This automatic pipeline welding system has been successfully used in several main pipeline projects in China, and has been approved by the constructors with the benefits of higher quality passing rate, higher welding efficiency and lower labor intensity.     

Lonworks现场总线技术及应用

本文对当前热点技术Lonworks现场总线技术的系统结构、技术特点以及应用等做了简要介绍。     

浅谈港口国监督的重要性(英文)

介绍港口国监督的发展历史,从保障海上人命安全和防止海洋污染方面详细阐述港口国监督的必要性.作为船旗国监督的补充和船舶航行安全的最后安全屏障,港口国监督有着不可替代的地位和责任.     

TCP网络的拥塞预测控制策略研究

针对Internet网络系统的拥塞控制问题,提出了一种基于预测函数的拥塞控制策略。在离散化传输控制协议(TCP)动态拥塞窗口模型基础上,将IP网络转化为具有约束的预测控制,采用预测函数算法策略进行优化求解,使队列快速平稳地到达目标值。仿真结果表明该方法能够适应复杂的网络环境,控制品质优于RED算法和PID算法,具有较好的稳定性和鲁棒性,有效减少了网络不确定性带来的不利影响。     

SNMPv3的安全机制

随着互联网的发展，网络安全逐渐成为网络管理中的一个重要问题。本文在结合对ＳＮＭＰｖ３（简单网络管理协议第３版）进行分析的基础上，介绍了其安全机制的两个模块及其安全处理过程。     

基于实验设计的健壮参数控制方法

针对复杂制造过程中无法应用自动过程控制理论,提出一种基于实验设计的参数健壮控制方法.该方法基于在线噪声变异的可观测且充分利用在线控制参数实时补偿变异的能力,在新的因素分类基础上,以田口损失函数最小为目标,建立了基于实验设计的健壮参数控制模型.并针对该模型提出了一种优化分析方法,拓展了实验设计回归模型的应用范围,使之能够适用于自动过程控制,从而形成复杂制造过程健壮参数控制的新方法.通过实验分析,验证了上述方法是有效且实用的.     

自整定内模PID控制在非圆销孔加工中的应用

将工业过程控制方法用于微位移压电陶瓷驱动器控制,提出了一种自整定内模PID控制方法.该方法结合内模控制、PID控制和前馈控制,针对非圆销孔加工的特点,采用双环的控制结构,内环主要对控制对象进行等效,提高其动态性能;外环通过时间乘绝对误差的积分(ITAE)优化内模PID的整定参数.鲁棒性分析、仿真及实验表明,系统具有很强鲁棒性,能快速响应,在工作频率低于20 Hz时具有较好跟踪性能,达到了快速跟踪和减少滞环的目的.     

中厚板轧机液压辊缝控制系统研究及其PLC实现

基于某中厚板轧机液压AGC计算机控制系统的设计和调试实践,对液压辊缝控制系统(HAPC)控制模型进行了理论研究,介绍了用PLC实现HAPC控制的系统配置,对采用PLC进行HAPC控制遇到的一些特殊问题进行了详细阐述·开创了PLC系统用于HAPC的先例,对于类似系统设计有一定的借鉴意义·     

智能混合动力汽车经济性自适应巡航控制策略研究

针对智能混合动力汽车自适应巡航过程中的能量控制策略问题,结合模型预测控制在处理多目标、多约束优化问题方面的优势和粒子群算法运算量小、收敛快的特点,将粒子群算法作为模型预测控制的滚动优化方法,构造基于模型预测控制的粒子群算法.仿真结果表明,文中算法能够使绝大部分工况点落在较低燃油消耗率区域,只有少部分工况点落在非经济区域,虽然多消耗了1.06%的燃油,但在运算速度上却获得了60.3%的提升.     

多层网TCP-RED非线性流体模型的高效网络拥塞控制

提出了一种多层网络拥塞控制方法,该方法基于流体模型的微分方程设计多层网络拥塞控制.此外,该方法利用流体模型的稳定性保证网络性能参数的均值在多层网络环境及大数据流的情况下向平衡点收敛,同时也有效限制了AOM参数的设置.理论分析和仿真结果证明了这种网络拥塞控制方法能在多层网络中避免拥塞,并当网络出现大数据流时,该多层AQM系统能充分利用网络带宽及保持路由器队列长度的相对稳定.     

一种集成SPC与EPC的过程控制方法

在多品种、小批量生产模式下,不合格品经常发生在由一种水平过渡到另一种水平的暂态阶段.采用由一阶动态过程和稳态干扰ARMA(1,1)组成的系统模拟制造过程,针对当输入发生阶跃变化时过程出现的暂态阶段,设计了集成统计过程控制(SPC)与工业过程控制(EPC)的过程控制方法.以往,SPC与EPC是减少过程波动的两种截然不同的方法,而集成控制方法是应用ARMA控制图对制造过程进行监视,同时PI控制器对过程进行调整.模拟结果表明:集成控制方法不但能减少过程输出波动,提升系统性能;并且在调整涉及成本因素时,集成控制方法更具有潜在的成本优势.