Discretization method for semi-definite programming

Semi-definite programs are convex optimization problems arising in a wide variety of applications and the extension of linear programming. Most methods for linear programming have been generalized to semi-definite programs. This paper discusses the discretization method in semi-definite programming. The convergence and the convergent rate of error between the optimal value of the semi-definite programming problems and the optimal value of the discretized problems are obtained. An approximately optimal division is given under certain assumptions. With the significance of the convergence property, the duality result in semi-definite programs is proved in a simple way which is different from the other common proofs.     

Learning linear PCA with convex semi-definite programming

The aim of this paper is to learn a linear principal component using the nature of support vector machines (SVMs). To this end, a complete SVM-like framework of linear PCA (SVPCA) for deciding the projection direction is constructed, where new expected risk and margin are introduced. Within this framework, a new semi-definite programming problem for maximizing the margin is formulated and a new definition of support vectors is established. As a weighted case of regular PCA, our SVPCA coincides with the regular PCA if all the samples play the same part in data compression. Theoretical explanation indicates that SVPCA is based on a margin-based generalization bound and thus good prediction ability is ensured. Furthermore, the robust form of SVPCA with a interpretable parameter is achieved using the soft idea in SVMs. The great advantage lies in the fact that SVPCA is a learning algorithm without local minima because of the convexity of the semi-definite optimization problems. To validate the performance of SVPCA, several experiments are conducted and numerical results have demonstrated that their generalization ability is better than that of regular PCA. Finally, some existing problems are also discussed.     

Robust differential game guidance laws design for uncertain interceptor-target engagement via adaptive dynamic programming

In this paper, the problem of intercepting a maneuvering target is formulated as a two-player zero-sum differential game framework affected by matched uncertainties. By introducing an appropriate cost function that reflects the uncertainties, the robust control is transformed into a two-player zero-sum differential game control problem and therefore ensures the compensation of the matched uncertainties. Additionally, the corresponding Hamilton--Jacobi--Isaacs (HJI) equation is solved by constructing a critic neural network (NN). The closed-loop system and the critic NN weight estimation error are proved to be uniform ultimate boundedness (UUB) by utilising Lyapunov approach. Finally, the effectiveness of the proposed robust guidance law is demonstrated by using a nonlinear two-dimensional kinematics, assuming first-order dynamics for the interceptor and the target.     

Adaptive dynamic programming for linear impulse systems

We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming （ADP） method. For linear impulse systems, the optimal objective function is shown to be a quadric form of the pre-impulse states. The ADP method provides solutions that iteratively converge to the optimal objective function. If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states, the objective function iteratively converges to the optimal one through ADP. Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible, the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases. A neural network based ADP method can circumvent this problem. A neural network with polynomial activation functions is selected to approximate the pr~impulse objective function and trained iteratively using the ADP method to achieve optimal control. After a successful training, optimal impulse control can be derived. Simulations are presented for illustrative purposes.     

Empirical analysis of GUI programming concerns

The focus of this paper is on identification of typical graphical user interface (GUI) programming concerns. As opposed to some other proposals available in the literature that indicate GUI programming concerns by simple intuition, we have conducted a systematic empirical analysis to derive our proposal. It included an analysis of an existing application programming interface (API), its use in industrial projects, and an analysis of the requirements and issues reported during software maintenance. In addition, we have evaluated more than 50 GUI frameworks and APIs and proved usefulness and generality of our classification of concerns. As an additional proof of applicability of the proposed classification, we have refactored the inheritance hierarchy of the selected GUI API using concern-oriented interfaces. We have implemented a supporting tool that complements the developed API and supports its concern-oriented use. The evaluation of the refactored API showed positive effects on API usability.     

Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP)

Symbolic Regression (SR) analysis, employing a genetic programming (GP) approach, was used to analyse laboratory strength and elasticity modulus data for some granitic rocks from selected regions in Turkey. Total porosity (n), sonic velocity (vp), point load index (Is) and Schmidt Hammer values (SH) for test specimens were used to develop relations between these index tests and uniaxial compressive strength (σ_c), tensile strength (σ_t) and elasticity modulus (E). Three GP models were developed. Each GP model was run more than 50 times to optimise the GP functions. Results from the GP functions were compared with the measured data set and it was found that simple functions may not be adequate in explaining strength relations with index properties. The results also indicated that GP is a potential tool for identifying the key and optimal variables (terminals) for building functions for predicting the elasticity modulus and the strength of granitic rocks.     

Decentralised zero-sum differential game for a class of large-scale interconnected systems via adaptive dynamic programming

In this paper, a novel decentralised differential game strategy for large-scale nonlinear systems with matched interconnections is developed by using adaptive dynamic programming technique. First, the Nash-equilibrium solutions of the corresponding isolated differential game subsystems are found by appropriately redefining the associated cost functions accounting for the bounds of interconnections. Then, the decentralised differential game strategy is established by integrating all the modified Nash-equilibrium solutions of the isolated subsystems to stabilise the overall system. Next, the solutions of Hamilton–Jacobi–Isaaci equations are approximated online by constructing a set of critic neural networks with adaptation law of weights. The stability analysis of each subsystem is provided to show that all the signals in the closed-loop system are guaranteed to be bounded by utilising Lyapunov method. Finally, the effectiveness of the proposed decentralised differential game method is illustrated by a simple example.     

Performance analysis of gradient neural network exploited for online time-varying quadratic minimization and equality-constrained quadratic programming

In this paper, the performance of a gradient neural network (GNN), which was designed intrinsically for solving static problems, is investigated, analyzed and simulated in the situation of time-varying coefficients. It is theoretically proved that the gradient neural network for online solution of time-varying quadratic minimization (QM) and quadratic programming (QP) problems could only approximately approach the time-varying theoretical solution, instead of converging exactly. That is, the steady-state error between the GNN solution and the theoretical solution can not decrease to zero. In order to understand the situation better, the upper bound of such an error is estimated firstly, and then the global exponential convergence rate is investigated for such a GNN when approaching an error bound. Computer-simulation results, including those based on a six-link robot manipulator, further substantiate the performance analysis of the GNN exploited to solve online time-varying QM and QP problems.     

遗传规划在符号回归中的应用

遗传规划(GP)是一种基于达尔文进化理论的数学规划方法。讨论了GP在符号回归中的应用。与传统的数据拟合方法相比,GP不必给出拟合函数的形式,同时,在初始群体足够大而且交叉和变异概率设置合理的情况下,不会陷入局部优化,具有更广泛的适用性。对于不给定函数形式的曲线拟合,GP可以自动得到曲线的函数形式及其参数大小,避免了传统方法的缺陷。通过具体的应用实例,说明了GP在测量数据处理中的应用。     

移动Ad hoc网络服务发现协议

介绍移动Ad hoc网络的应用前景和主要研究内容,阐述服务协议的重要性以及服务发现协议中的基本概念,针对现有的移动Ad hoc网络服务发现协议的核心技术和设计思想进行分析,选取几种典型的服务发现协议进行对比,总结得出各类服务发现协议的优缺点和适用范围,并指出该领域的进一步研究方向。     

仪器仪表传感器修正系数的现场编程技术

现场编程 (in applicationprogramming)是通过微机系统上的I/O口对系统进行编程的方法 ,这种方法对工业仪器仪表中传感器修正系数的更换带来了方便 ,特别是对具备远程下载能力的系统 ,其传感器修正系数的更换可以通过通讯网络来实现。以通用微处理器为CPU的工业仪器仪表中可以实现现场编程的功能     

A dynamic programming approach: Improving the performance of wireless networks

Traditional wireless networks focus on transparent data transmission where the data are processed at either the source or destination nodes. In contrast, the proposed approach aims at distributing data processing among the nodes in the network thus providing a higher processing capability than a single device. Moreover, energy consumption is balanced in the proposed scheme since the energy intensive processing will be distributed among the nodes. The performance of a wireless network is dependent on a number of factors including the available energy, energy–efficiency, data processing delay, transmission delay, routing decisions, security architecture etc. Typical existing distributed processing schemes have a fixed node or node type assigned to the processing at the design phase, for example a cluster head in wireless sensor networks aggregating the data. In contrast, the proposed approach aims to virtualize the processing, energy, and communication resources of the entire heterogeneous network and dynamically distribute processing steps along the communication path while optimizing performance. Moreover, the security of the communication is considered an important factor in the decision to either process or forward the data. Overall, the proposed scheme creates a wireless “computing cloud” where the processing tasks are dynamically assigned to the nodes using the Dynamic Programming (DP) methodology. The processing and transmission decisions are analytically derived from network models in order to optimize the utilization of the network resources including: available energy, processing capacity, security overhead, bandwidth etc. The proposed DP-based scheme is mathematically derived thus guaranteeing performance. Moreover, the scheme is verified through network simulations.     

基于条件云的基因表达式编程算法

标准基因表达式编程算法(GEP)在挖掘知识时采用恒定的变异和交叉率,没有考虑进化中个体适应度的变化,依然存在难以摆脱局部最优和收敛速度问题.为了解决这一问题,提出了将X条件云模型应用到基因表达式编程的算法(XCC-GEP).该算法在进化前期采用固定变异率和交叉率;一旦处于收敛状态时,根据个体的当前适应度,借助X条件云,动态调整其变异率和交叉率,以跳出早熟收敛.实验表明了算法的有效性.     

基于分布式粗粒度并行计算的遗传规划算法研究

针对经典遗传规划算法(CGP)存在容易早熟收敛、运行效率低的缺陷,提出一种将分布式计算与遗传规划算法结合的计算模型.该模型利用个体迁移策略实现对种群的优化,克服易早熟的缺陷.并且采用分布式计算能够有效地节省算法的运行时间.最后通过对语音数据预测误差的比较,验证了改进后算法的有效性.实验表明,基于分布式粗粒度并行计算的遗传规划算法(CGGP)计算性能优于经典遗传规划算法(CGP).     

云虚拟网络安全研究

云计算以虚拟化技术为基础,提供了一种按需、灵活分配资源的网络计算模式。在网络虚拟化技术的推动下,用户的网络变为云服务提供商根据用户需求,在物理网络之上为其分配的逻辑上相互隔离的虚拟网络。虚拟网络带来了网络架构的动态性,呈现出网络边界动态模糊、共享底层资源及流量以内部“东西”向交互为主的新特性,不仅加剧了传统网络固有的攻击威胁(如ARP攻击、DoS攻击等),还引入了新的安全威胁:虚拟网络边界防护失效、信息泄露及篡改、流量监控存在盲点等。因此,虚拟网络的安全问题成为工业界和学术界关注的热点。本文对虚拟网络环境中存在的安全问题进行了归纳,分析产生的原因,给出了云虚拟网络的威胁模型;并针对这些安全问题,从基于虚拟防火墙、基于安全服务动态部署、基于虚拟网络嵌入、基于虚拟网络隔离强化、基于深度流量监测、基于流量动态控制等类别分别对近年国内外相关防御机制进行了分析和比较,并指出了当前仍存在的问题;最后对虚拟网络安全未来研究方向进行了探讨,给出了基于软件定义边界的动态防御框架。     

GeoVISTA Studio: a codeless visual programming environment for geoscientific data analysis and visualization

The fundamental goal of the GeoVISTA Studio project is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computationally and visually based. Improving the infrastructure used in analysis has far-reaching potential to better integrate human-based and computationally based expertise, and so ultimately improve scientific outcomes. To address these challenges, some difficult system design and software engineering problems must be overcome.This paper illustrates the design of a component-oriented system, GeoVISTA Studio, as a means to overcome such difficulties by using state-of-the-art component-based software engineering techniques. Advantages described include: ease of program construction (visual programming), an open (non-proprietary) architecture, simple component-based integration and advanced deployment methods. This versatility has the potential to change the nature of systems development for the geosciences, providing better mechanisms to coordinate complex functionality, and as a consequence, to improve analysis by closer integration of software tools and better engagement of the human expert. Two example applications are presented to illustrate the potential of the Studio environment for exploring and better understanding large, complex geographical datasets and for supporting complex visual and computational analysis.     

基于网络编码的定向扩散路由协议

提出一种新的基于网络编码的无线传感器网络定向扩散路由协议。在定向扩散路由建立的过程中利用随机线性网络编码,使中继节点对收到的分组进行网络编码后再转发,以减少网络中分组传输的次数,优化网络带宽利用率。基于网络仿真平台NS2的仿真结果表明,新协议的网络性能得到有效改善,节点的平均剩余能量和网络吞吐量有显著提高。