矢量网络分析仪测量不确定度评估算法

虽然目前校准算法已趋成熟,但由于随机误差和剩余系统误差等的存在,矢量网络分析仪(Vector network analyzer,VNA)的测量结果不可避免地会有一定的不准确性.而现有的矢网测量不确定度评估算法中往往只考虑剩余系统误差,忽略了系统线性性能、线缆状态以及测试环境等因素.本文同时考虑了校准后剩余系统误差、系统非线性误差,随机误差及测量环境等因素,建立了整机测量不确定度模型,实现了矢量网络分析仪整机测量不确定度的评估.与现有矢网不确定度评估算法相比,本算法考虑的误差因素更为全面,评估结果更为可靠.利用误差上限传递思想推导出商用VNA散射参数测量不确定度评估公式,并提供了相应的参数获取方案.应用该算法对安捷伦8753ES矢量网络分析仪进行测量不确定度评估,并与安捷伦提供的技术数据进行对比,其结果数据吻合良好.     

基于CCA的入侵检测行为轮廓创建技术研究

入侵检测系统在保障Internet应用系统安全方面发挥着重要作用.作为异常检测依据,用户行为轮廓的准确程度直接关系到入侵检测系统的检测性能.由于Internet环境的开放性造成用户行为模式多变,导致用户行为轮廓准确程度下降.本文提出了基于信息可视化的入侵检测框架,并进一步提出了基于CCA(Curvilinear component analysis)的可视化算法.该可视化算法比传统算法具有更好的距离映射性能,可为安全专家提供准确的可视信息,有利于安全专家直观地观察用户行为模式,并合理选择聚类算法创建轮廓,从而提高行为轮廓创建的准确性.     

Uncertain decision tree inductive inference

Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.     

高功率微波辐射场功率密度测量不确定度分析方法

辐射场测量是检验高功率微波系统输出指标的重要手段.随着高功率微波测量技术的发展,辐射场测量系统的稳定性和可靠性不断提高,作为完整测量结果重要组成部分的测量不确定度越来越受到关注.文章介绍了高功率微波辐射场功率密度测量方法及系统组成,建立了功率密度测量的数学模型,给出了高功率微波辐射场功率密度测量的主要不确定度来源.对检波器输入功率计算、接收天线有效面积校准、衰减环节校准及测量系统各环节连接失配等测量不确定度分量进行了分析,并给出了高功率微波辐射场功率密度测量不确定度的合成方法.本文给出的测量不确定度分析方法较为科学、操作性强,对完善高功率微波辐射场功率密度测量结果具有一定的指导意义.     

基于不确定度的组合导航数据动态评定

对于动态测量过程中的双导航系设备(系统)输出数据，所获得的船位由于其各自的不确定度不同，融合后所得的船位应为双导航设备给出船位的加权平均。本文给出了基于序列参数测量过程的双导航设备输出数据融合的不确定度评定，使由双导航设备组成的组合导航系统的导航参数输出具有更好的可信度。给出了说明该方法是可行的应用实例。其结论可推广至多导航设备(系统)的组合导航输出参数的评定。     

新型复杂进化全局优化算法的研究

文章在复杂系统思想激励下设计了一种新型的基于复杂系统改进的进化算法,该算法改进了进化算法的交叉、选择、变异和进化策略,体现了进化过程中能量分布、空间搜索、信息利用的复杂性,并保持了进化算法的简单结构框架;进而通过计算实例分析了新型复杂系统进化算法两个主要参数对算法性能影响;最后,新算法测试了CEC'2012大规模全局优化竞赛中的函数集合,并与其他优秀算法的测试结果进行了对比,结果表明本文所提出的基于复杂系统改进的进化算法综合性能强于所有的对比算法.     

基于不确定性推理的JPEG图像通用隐藏信息检测技术

目前隐写分析通常被作为一种确定性问题进行研究,研究中忽略了不确定性因素的影响,这导致了检测可靠性下降.本文对通用隐写分析中的不确定性因素进行了分析.在此基础上,构建了基于不确定性推理的隐写分析模型,设计了基于证据推理的通用隐写分析算法.实验证明了算法具有较好的可扩展性和可靠性,从而也验证了使用不确定性推理方法解决隐写分析问题的有效性.     

基于嵌套张量模型的MIMO中继系统组合接收算法

目前在单向双跳多输入多输出(MIMO)中继系统中,基于嵌套张量模型的接收算法主要采用单步交替最小二乘(ALS)和KRF(Khatri-Rao Factorization)算法.在时变信道且实时性要求较高场景下,计算复杂度高是制约其应用的主要因素.为此,在对单向双跳MIMO中继系统建模基础上,提出了基于嵌套张量模型的双步组合接收算法.该算法通过对接收的数据张量进行重建,将符号估计和信道估计分离,充分利用ALS和KRF的算法优势,有效降低了计算复杂度.同时,对算法的可辨识性进行了分析.仿真结果表明,该算法保持了与传统嵌套PARAFAC的最小二乘(Nested PARAFAC ALS)算法的相同估计性能,在源天线个数变化时,计算复杂度降低了80%以上;在中继天线个数变化时,计算复杂度降低了50%以上.     

Turbo processing in systems with high-efficient space-time coding

Construction principles for algorithms intended to perform space-time and turbo processing of signals and to improve the qualitative characteristics of high-rate data transmission systems have been discussed. The algorithm for space—time processing of signals transmitted by the system with several transmitting and several receiving antennas—which relies on the analysis of principles applied to the construction of known transmission systems with high spectral efficiencies and makes it possible to enhance the reliability of information transmission without deteriorating the system throughput—is proposed. A new iterative algorithm for signal processing at the receiver has been developed to implement the advantages of the proposed method of space—time processing of signals. The results of computer simulation that confirm the great potentiality of proposed algorithms are presented.     

New peak-to-average power-ratio reduction algorithms for multicarrier communications

New peak-to-average power-ratio (PAPR) reduction algorithms for multicarrier systems are developed by modifying the modulation constellation in active subcarriers and the modulation symbols in unused subcarriers. The proposed algorithms yield optimal PAPR-reduction solutions. For real-baseband multicarrier systems, the proposed PAPR-reduction algorithm is developed using a fast linear programming approach and considerable performance improvement can be achieved relative to that achieved with several existing algorithms. For passband multicarrier systems, a new PAPR-reduction algorithm is constructed whereby the associated minimax optimization problem is solved using an accelerated least-p th algorithm. Simulation results are presented which demonstrate that the proposed algorithm outperforms an algorithm due to Jones and that improved PAPR reduction can be achieved when the proposed algorithm is combined with another algorithm known as selective mapping scheme.     

基于动态不确定度评定的加权数据融合算法

加权融合算法的AR模型,采用遗忘因子递推最小二乘法估计出测量方差.根据测量方差对单一导航传感器信息进行动态不确定度评定,进而确定组合导航系统的组合不确定度.在满足组合不确定度最小的情况下,利用拉格朗日乘数法进行加权值的求解.通过实例仿真表明该法具有较高的融合精度,对提高组合导航系统的定位精度具有实际意义.     

Robust Model Predictive Control for Piecewise Affine Systems

In this paper, we study a robust model predictive control (MPC) strategy for piecewise affine (PWA) systems with uncertainty that is described as a set of polytopic parameter-varying models in a polytope corresponding to each partition of the PWA systems. First, an infinite horizon MPC technique for guaranteeing robust stability is developed for uncertain PWA systems. According to the condition of the PWA system states, the sequence of piecewise linear feedback controller at each sampling time is derived on-line by solving a convex optimization problem involving linear matrix inequalities. The feasible PWA control law design can robustly stabilize the uncertain PWA systems. However, the on-line optimization problems may lead to a computational burden. Then we further propose an improved robust MPC algorithm. When the current state is outside of the region of PWA systems containing the origin, the proposed on-line robust MPC algorithm is utilized; once the current state enters the region with the origin, sequence attraction domains where the origin is included are constructed off-line one within another, and the explicit control laws corresponding to different attraction domains can drive the state to the origin. The two algorithms are illustrated with a numerical example. The simulation results show that both controller design methods can stabilize the PWA systems with polytopic uncertainty, and the improved algorithm can reduce the on-line computation cost.     

Value‐centric framework and pareto optimality for design and acquisition of communication satellites

Investments in space systems are substantial, indivisible, and irreversible, characteristics of high‐risk investments. Traditional approaches to system design, acquisition, and risk mitigation are derived from a cost‐centric mindset, and as such they incorporate little information about the value of the spacecraft to its stakeholders. These traditional approaches are appropriate in stable environments. However, the current technical and economic conditions are distinctly uncertain and rapidly changing. Consequently, these traditional approaches have to be revisited and adapted to the current context. We propose that in uncertain environments, decision‐making with respect to design and acquisition choices should be value‐based. We develop a value‐centric framework, analytical tools, and an illustrative numerical example for communication satellites. Our two proposed metrics for decision‐making are the system's expected value and value uncertainty. Expected value is calculated as the expected NPV of the satellite. The cash inflow is calculated as a function of the satellite loading, its transponder pricing, and market demand. The cash outflows are the various costs for owning and operating the satellite. Value uncertainty emerges due to uncertainties in the various cash flow streams, in particular because of market conditions. We propagate market uncertainty through Monte Carlo simulation, and translate it into value uncertainty for the satellite. The end result is a portfolio of Pareto‐optimal satellite design alternatives. By using value and value uncertainty as decision metrics in the down‐selection process, decision‐makers draw on more information about the system in its environment, and in making value‐based design and acquisition choices, they ultimately make more informed and better choices. Copyright © 2009 John Wiley & Sons, Ltd.     

基于偶然不确定性的单人姿态估计模型测试时增强算法

针对现有单人姿态估计网络结果缺乏可靠性评估和鲁棒性保障等问题,提出了一种基于偶然不确定性的测试时增强方法。该方法首先利用随机并行的数据增强和模型推理得到多样化输出,随后通过计算该输出的偶然不确定性得到其可靠性评估,最后根据可靠性将该输出及其不确定性进行加权融合以得到更准确鲁棒的结果及其评估。在MPII数据集上的实验表明,该算法可即插即用地应用于任意现有单人姿态估计网络,从而得到更精确鲁棒的结果及其不确定性评估。     

System Entropy and Its Application in Feature Selection

1　IntroductionFeatureselection ,ideally ,istoselecttheopti malfeaturesubsetfromacandidatesettodescribethetargetconception .Peopleusuallypaymuchat tentiontofeatureselectionbecauseofitspotentialofsimplifyingthestructureofasystem ,speedinguptheprocessofruleinduction ,reducingthecostofinstanceclassificationandimprovingtheperfor manceofthelearnedresults.Theoptimalfeaturesubsetofasystemisusuallymini featurebiased ,i.e.itprefersdescribingasystemwithfeaturesasfewaspossible[1 ] .Unfortunately ,theprob…     

混合数据的邻域区分度增量式属性约简算法

增量式属性约简是一种针对动态环境下的数据挖掘方法.目前已经提出的增量式属性约简算法仅适用于符号型的信息系统,而很少有对混合信息系统进行相关的研究,这促使在混合信息系统下构建相关的增量式属性约简算法.区分度是用于设计属性约简的一种重要方法,本文将传统的区分度在混合信息系统下进行推广,提出邻域区分度的概念,然后分别研究了邻域区分度在混合信息系统下对象增加和对象减少时的增量式学习,最后根据这种增量式学习分别提出了对应的增量式属性约简算法.UCI数据集上的相关实验结果表明,所提出的增量式属性约简比非增量式属性约简能够更快速的更新约简结果.     

一种基于滤波的分布式任务分配算法

尽管同步模式协同算法允许团队在许多通用网络拓扑中达成一致状态感知并执行任务分配,但收敛到一致状态感知需要花费大量时间,传输大量数据。在低带宽环境中这会导致严重的潜在问题,增加求解任务方案时间。分析了现存协同拍卖算法例如、CBBA,HIPC等的优缺点,并对HIPC进行改进。采用将滤波框架引入分布式协同拍卖的方法,在建立任务束阶段采用滤波算法对局部状态感知进行估计。打破了HIPC对局部场景感知的完美假设,使得新算法可以在局部感知不可靠条件下良好运行,该算法可以异步进行并行任务分配和冲突分解。通过试验得出相比于HIPC,新算法减少了任务冲突次数,缩短任务执行时间。     

基于四阶累积量的非圆信号测向方法

对通信系统中大量使用的BPSK等非圆信号测向,可以采用共轭扩展MUSIC(CE-MUSIC)算法,也可以采用基于四阶累积量的MUSIC-like算法。CE-MUSIC算法没有利用高阶信息,MUSIC-like算法没有利用信号的非圆信息,性能均受限。该文提出的四阶扩展MUSIC(FO-EMUSIC)算法利用了非圆信号在四阶累积量中的信息,分辨力和测角精度明显优于MUSIC-like算法,略优于CE-MUSIC算法,可测向阵元数大于CE-MUSIC算法和MUSIC-like算法。针对均布线阵,为减小计算量,还提出了FO-EMUSIC/ULA算法。仿真实验验证了FO-EMUSIC算法的优良性能。     

Resonant frequency estimation for adaptive notch filters in industrial servo systems

This paper proposes a new algorithm for estimating the resonant frequency of adaptive notch filters used in servo systems. Notch filters and adaptive notch filters are widely used in commercial servo systems for suppressing a resonance which is a major obstacle in improving their performance. However, the conventional frequency estimation algorithm gives a dynamic behavior that is proportional to the difference between the square of the estimated frequency and the square of the actual frequency. This can cause the estimation dynamics to be too slow for low-frequency resonances, if both low- and high-frequency resonances are present and if the estimator gain is designed for a high frequency. This paper develops a new algorithm to give a dynamic behavior that is proportional to the difference in the estimated frequency and actual frequency. This allows selecting the estimation parameters independent from the value of resonant frequencies. The developed algorithm is implemented to a production servo controller and applied to a production printed-circuit-board inspection system. The experimental results show that the developed algorithm is much more effective in suppressing the resonances of both low- and high-frequencies, compared to the pervious algorithm. Furthermore, the ability to suppress vibration allows increasing the feedback gain, which in turn allows improving the tack time performance from 190 ms to 100 ms. All experiments reported in this paper were performed in an actual industrial environment using a production system, and the developed algorithms are applied to the production system.     

Generalized binary discernibility matrix for attribute reduction in incomplete information systems

To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.