LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.     

Blind channel estimation for multiple antenna OFDM system subject to unknown carrier frequency offset

The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） is addressed. Multiple signal classification （MUSIC）-Iike algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional （RD）-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramer-Rao bound （CRB） of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.     

Consensus algorithm for multiple quadrotor systems under fixed and switching topologies

The distributed leadless consensus problem for multiple quadrotor systems under fixed and switching topologies is investigated. The objective is to design protocols achieving consensus for networked quadrotors＇ positions and attitudes. Because the model of a quadrotor is a strong high-order nonlinear coupling system, the approach of feedback linearization is employed to transform the model into a group of four linear subsystems among which there is no coupling. Then, a consensus algorithm is proposed which consists of a local feedback controller and interactions from the finite neighbors under fixed undirected topologies. Especially, the problem of choosing the parameters in the consensus algo-rithm is also addressed, enlightened by the results of the robust control theory. Furthermore, it is proved that the proposed algo-rithm also guarantees the consensus under undirected switching topologies. Simulation results show the effectiveness of the pro- posed algorithm.     

High-resolution swath bathymetry using MIMO sonar system

For increasing the cross-track resolution, the multiple input multiple output （MIMO） technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIMO sonar is composed of two parallel transmitting uniform linear arrays （ULAs） and a receiving ULA which is perpendicular to the former. The spacing between the two transmitting ULAs is equal to the product of the receiving sensor number and the receiving inter-sensor spacing. Furthermore, two narrowband linear frequency modulation （LFM） pulses, sharing the same frequency band but with opposite modulation slopes, are used as transmitting waveforms of the two transmitting ULAs. With such an array layout and transmitting signals, the MIMO sonar can sound a swath with the cross-track resolution doubling that of the traditional multibeam sonar using a Mills cross array. Numerical examples are provided to verify the effectiveness of the proposed approach.     

MIMO-DCSK communication scheme and its performance analysis over multipath fading channels

The differential chaotic shift keying （DCSK） communication in multiple input multiple output （MIMO） multipath fading chan- nels is considered. A simple MIMO-DCSK communication scheme based on orthogonal multi-codes （OMCs） and equal gain combination （EGC） is proposed, in which OMCs are used to spread the same information bit at each transmitting antenna and the infor- mation bit is detected by EGC at receiving antenna. The OMCs are constructed from one chaotic sequence by means of othogo- nal space-time block coding （OSTBC）. The output signal-to-noise ratio （SNR） after EGC is given based on central limit theory （CLT）, and it can effectively exploit the spatial diversity of the underlying MIMO system. Simulation results show that the full spatial diversity gain is achieved without channel estimation in the MIMO-DCSK communication scheme and it performs better than MC-EGC for a large number of transmitting antennas.     

虚拟样机可视化仿真服务的研究与实现

为了实现虚拟样机仿真运行过程中可视化仿真资源的共享,将可视化技术与Web技术相结合,采用B/S架构和浏览器端显示技术,开发通用的可视化仿真功能模块以及相应的实时数据接口,实现了虚拟样机可视化仿真服务,使远程仿真用户能根据可视化仿真需求配置和请求可视化资源,定制二维地理信息显示/三维视景显示服务,从而在浏览器上快速创建虚拟样机二维/三维可视化仿真任务并实时运行,对可视化仿真服务的框架结构、关键技术及应用进行了相关研究和实现。     

Reduced K-best sphere decoding algorithm based on minimum route distance and noise variance

This paper focuses on reducing the complexity of K-best sphere decoding （SD） algorithm for the detection of uncoded multi-ple input multiple output （MIMO） systems. The proposed algorithm utilizes the threshold-pruning method to cut nodes with partial Euclidean distances （PEDs） larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smal est PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service （QoS） and the signal-to-noise ratio （SNR） bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.     

Modified MUSIC estimation for correlated signals with compressive sampling arrays

This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.     

Cramer-Rao bound of joint estimation of target location and velocity for coherent MIMO radar

The optimal estimation performance of target parameters is studied. First, the general form of Cramer-Rao bound （CRB） for joint estimation of target location and velocity is derived for coherent multiple input multiple output （MIMO） radars. To gain some insight into the behavior of the CRB, the CRB with a set of given orthogonal waveforms is studied as a specific case. Second, a maximum likelihood （ML） estimation algorithm is proposed. The mean square error （MSE） of the ML estimation of target location and velocity is obtained by Monte Carlo simulation and it approaches CRB in the high signal-to-noise ratio （SNR） region.     

Ranking grey numbers based on dominance grey degrees

With respect to the decision making problems where a lot of fuzzy and grey information always exists in the real-life decision making information system methods as fuzzy mathematics, it is difficult for such uncertainty probability, and interval numbers to deal with. To this end, based on the thought and method of grey numbers, grey degrees and interval numbers, the concept of dominance grey degree is defined. And then a method of ranking interval grey numbers based on the dominance grey degree is proposed. After discussing the relevant properties, the paper finally uses an example to demonstrate the effectiveness and applicability of the model. The result shows that the proposed model can more accurately describe uncertainty decision making problems, and realize the total ordering process for multiple-attribute decision-making problems.     

面向大规模信息可视化分析的空间优化交互方法

本文针对焦点上下文变形方法对大规模信息布局进行交互时,出现的焦点放大后更多的信息被压缩显示在更小的空间,整体布局被严重扭曲的问题,提出了一种空间优化交互方法.这种方法针对大规模信息的几何布局进行变形,扩大焦点区域分配的空间,所有节点在新分配的空间内按照原布局方法进行布局.变形过程中能消除点边交叉,变形后能清晰地保持大规模信息整体布局.将这种交互方法应用于大规模疫情传播的可视化布局中,实验效果表明,在保存清晰的整体布局同时放大关注区域,可以辅助用户更好地进行可视化分析.     

Optimized LDPC differential-encoded 16QAM scheme for high-speed transmission systems

The 16-ary quadrature amplitude modulation （16QAM） is a high spectral efficient scheme for high-speed transmission systems. To remove the phase ambiguity in the coherent detection system, differential-encoded 16QAM （DE-16QAM） is usually used, however, it will cause performance degradation about 3 dB as compared to the conventional 16QAM. To overcome the performance loss, a serial concatenated system with outer low density parity check （LDPC） codes and inner DE-16QAM is proposed. At the receiver, joint iterative differential demodulation and decoding （ID） is carried out to approach the maximum likelihood performance. Moreover, a genetic evolution algorithm based on the extrinsic information transfer chart is proposed to optimize the degree distribution of the outer LDPC codes. Both theoretical analyses and simulation results indicate that this algorithm not only compensates the performance loss, but also obtains a significant performance gain, which is up to 1 dB as compared to the conventional non-DE-16QAM.     

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.     

VARIABLE SELECTION FOR COVARIATE ADJUSTED REGRESSION MODEL

This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called ＂oracle property＂. In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.     

Direct adaptive control for a class of MIMO nonlinear discrete-time systems

This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor ex-pand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adap-tive neural network （NN） control er is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobal y uni-formly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.     

Web software reliability modeling with random impulsive shocks

As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large num- ber of software reliability growth models （SRGMs）, including those combined with multiple change-points （CPs）, have been available, these conventional SRGMs cannot be directly applied to web soft- ware reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random im- pulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website （www.icrms.cn）, the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.     

Expansion modelling of discrete grey model based on multi-factor information aggregation

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.     

基于Web的群体决策研讨信息自主可视化模型

在开放式群体决策研讨过程中,为了有效克服简单的文本记录方法和信息显示方式给群体交流带来的不便,克服在研讨过程中存在的“知识断层”和“信息过载”,改善交流效果并最终有效地辅助决策,需要探索集文本、音频和视频等多媒体为一体的研讨信息可视化显示方式。在分析群体成员对研讨信息可视化需求的基础上,提出了研讨信息自主可视化的概念,建立了基于UM L的群体研讨信息自主可视化模型。最后,给出了相应的实现模式和一个简单的群体研讨信息自主可视化平台示例。     

面向虚拟地理环境的三维可视化架构研究

提出了虚拟地理环境的可视化架构应该由三个部分组成:数据模型接口,模型集成接口和可视化流程管理;在将这三部分结合成一个完整体系的同时,分别讨论了三者在面向虚拟地理环境三维可视化架构中所起的作用,并且针对一些技术性难点提出了相应的解决方案。     

面向对象的交互显示和拾取模型研究

针对三维CAD／CAM系统可视化交互部分的实现，深入研究了可视化关键技术－－三维形体在计算机屏幕上显示和拾取的具体运行机制，采用面向对象思想构建了各自不同的模型框架，能够清楚高效地实现交互显示、拾取过程，简化系统结构，使系统具有层次性，易于维护，在此基础上，对显示和拾取进一步进行封装，合二为一，建立了两者统一的可视化数据模型，模型结构清晰，在面向对象环境下容易实现，体现了软件良好的封装性和复用性。