A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An AI-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.     

A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An Al-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.     

Intelligent control over chaos in the rolling process and its comparison with tracking control

Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.     

On parametric approach of aerial robots'' visual navigation

In aerial robots＇ visual navigation, it is essential yet very difficult to detect the attitude and position of the robots operated in real time. By introducing a new parametric model, the problem can be reduced from almost unmanageable to be partly solved, though not fully, as per the requirement. In this parametric approach, a multi-scale least square method is formulated first. By propagating as well as improving the parameters down from layer to layer of the image pyramid, a new global feature line can then be detected to parameterize the attitude of the robots. Furthermore, this approach paves the way for segmenting the image into distinct parts, which can be realized by deploying a Bayesian classifier on the picture cell level. Comparison with the Hough transform based method in terms of robustness and precision shows that this multi-scale least square algorithm is considerably more robust to noises. Some discussions are also given.     

Global minimization of adaptive local image fitting energy for image segmentation

The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.     

Stochastic framework for reliability enhancement using optimal feeder reconfiguration

Optimal distribution feeder reconfiguration （DFR） is a valuable and costless approach to increase the load balance, reduce the amount of power losses, and improve the voltage of the buses. In this way, this paper aims to investigate the optimal DFR strategy as a proper tool to improve the reliability of the radial distribution networks. The idea of failure rate reduction is employed to see the effect of feeder current reduction on the reliability of the system more accurately. The objects to be investigated are system average interruption frequency index （SAIFI）, system average interruption duration index （SAIDI）, average energy not supplied （AENS） and total active power losses. The problem is then formulated in a stochastic framework based on the point estimate method （PEM） to handle the uncertainty effects. The feasibility and satisfying performance of the proposed method is examined on a standard IEEE test system.     

Robust exponential stabilization of uncertain nonholonomic chained systems based on visual feedback

The visual serving stabilization for a kind of nonholonomic mobile robots with uncalibrated camera parameters is investigated based on the visual feedback and the state and input transformations. The authors obtain a new uncertain model of the nonholonomic kinematic system in the image plane,which is a chained form with uncalibrated visual parameters,from the camera robotic system. A new time varying feedback controller is proposed for the exponential stabilization of the nonholonomic chained system with unknown parameters by using state-scaling and switching technique.The exponential stability of the closed loop system is rigorously proved.Simulation results demonstrate the effectiveness of the proposed methods.     

Synchronization of chaos using radial basis functions neural networks

The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.     

A Study on System Availability Vs System Administration Efforts with Mathematical Models

Abstract: Two mathematical models are developed in this paper to study the effectiveness of system administration effortson the improvement of system availability, based on the assumption that there exists a transitional state for a computer sys-tem in operation before it is brought down by some hardware or software problems and with intensified system administra-tion efforts, it is possible to discover and fix the problems in time to bring the system back to normal state before it isdown. Markov chain is used to simulate the transition of system states. A conclusion is made that increasing system admin-istration efforts may be a cost-effective way to meet the requirements for moderate improvement on system availability, buthigher demand on this aspect still has to be met by advanced technologies.     

交通微观仿真中的驾驶员视觉感知模型

驾驶员视觉感知模型是交通微双肪真中机动车模型的—部分，其作用是表现出实际驾驶活动中，驾驶员对外界的视觉感知能力，也就是哪些物体可以被驾驶员看到，哪些不能。通过驾驶员视觉关注焦点的移动规律、视场范围和障碍物遮挡等3个方面的研究，提出了一个新的驾驶员视觉感知模型，并注意了该模型的运算速度问题。通过计算机模拟实际场景，对比受试者和模型标注的感知对象，完成了模型有效性验证。对10位受试者进行了194次测试，受试者标注了528个目标，而感知机模型标注了516个目标，其中吻合率为84．30％，漏检率17．61%，误检率15．34％。对出现差异的情况进行细致地分析，可以发现这些差异全部来源于受试者的感觉误差，而且受试者在看到视觉感知模型的输出后，没有一例表现出异议。此外，该模型在避免以往的理想全局明縻模式的带来的缺点的同时1还能使模型的计算量大为减少。     

基于粒子系统的实时雨雪模拟

雨雪效果的模拟可以大大提高飞行模拟器视景的逼真度，粒子系统是模拟雨雪效果的有效方法。在分析粒子系统的基础上，提出了一种模拟雨雪的实时算法。该算法的基本思想是将全部雨雪粒子分布于眼点前的视区内，高效率地利用有限的粒子来渲染场景。通过恰当选择粒子数量和粒子模型，该算法可以生成逼真的视觉效果并满足视景系统实时性的要求。     

基于摄像机运动的简单虚拟场景生成

利用摄像机标定技术和视频融合技术设计了一个基于摄像机运动的简单虚拟场景生成系统。首先对摄像机进行标定，然后利用摄像机的运动参数根据摄像机成像模型计算出3-D实景中特定的位置在2-D视频图像上的映射，最后利用映射结果对虚拟景物进行透视变换，并把它实时融合到视频图像特定位置中形成新的视觉效果，实现了一种类似于虚拟演播室的简单、有效、实用的视频制作新方法。实验表明该方法实时性好、生成的虚拟场景效果逼真。     

在线估计雅可比矩阵的视觉伺服控制算法

机器人视觉伺服系统是机器人研究领域的一个重要研究方向。以图像为基础的视觉伺服机器人模型中,有许多的不确定性,如机器人动力学模型,运动学模型,摄像机系统以及雅可比矩阵等。多数的视觉伺服系统往往只考虑某一、两个部分,而其它的不确定性依然影响定位目标的精度。在使用了神经网络的控制器中加入卡尔曼滤波,在摄像机与机器人坐标系无标定的情况下,对雅可比矩阵进行实时在线估计,从而提高了视觉伺服系统的精确性。仿真验证了本算法的可行性和有效性。

Abstract：

Robotic visual servo system is an important subject in the field of robots.Image-based visual servo model has a lot of uncertainties,for example,robot dynamics model,robot kinematics model,camera system and Jacobian matrix and so on.Most of visual servo methods always considered one or two aspects,but other uncertainties still affected the accuracy of tracking the target for the robot.Based on the traditional controller with neural network,a new control algorithm was provided in the uncalibrated camera and coordination systems with the Kalman filter that estimated Jacobian matrix on-line,thereby improving the accuracy of the system.Simulation result shows the feasibility and effectiveness of the algorithm.     

空间立体仿真视景的技术研究及实现

军机飞行仿真系统的视景系统即需要有纵深感、立体感，又要有大视场，针对这一要求，在“苏二七飞机飞行仿真系统”中，提出并采用了空间立体视景系统，本文论述了该系统的设计思想、实施方案及关键问题的解决方法。     

多视图像的三维重建并行计算仿真平台

针对基于视频图像的三维重建所需设备复杂和计算量大的问题,提出基于CUDA的可视外壳并行计算仿真实验平台。用三维图形绘制管线中的视点模拟真实相机,应用相机参数将各图像中物体轮廓坐标统一转换到世界坐标系,利用CUDA并行计算可视外壳。在此平台上将基于体素的可视外壳生成算法转化为CUDA线程块并行计算,仿真实验表明加速效果明显。     

基于自然特征点的实时增强现实注册算法

提出了一种基于自然特征点的实时跟踪注册算法,并将其应用于增强现实系统。算法在已知场景的3D模型以及少量标定关键帧图像的基础上,选择与当前图像最为匹配的关键帧,利用基于关键帧的图像匹配方法实时获取摄像机的运动参数估计。算法采用合成中间图像的技术解决两图像特征点间的宽基线匹配问题,并应用扩展卡尔曼滤波器对参数估计结果进行平滑,以消除系统抖动现象。实验结果表明,算法鲁棒性强,注册精度高,能够有效地克服系统误差漂移。     

视景仿真系统光学视觉通道技术研究与实现

研究了在视景仿真系统中应用多视觉通道图形显示技术时应注意的关键问题,仿真结果表明,多视觉通道技术的合理运用,能增加飞行视景仿真的“沉浸”感。结合该类系统的需要,提出了“光学通道”的概念。根据摄像机成像原理,得到了焦距与视场角的关系,实现了光学通道的变焦功能。该方法计算量小,实现简单,能满足仿真实时性的要求,具有重大的工程意义。     

轨道动力学可视化仿真研究

研究了在机车车辆一轨道耦合动力学视景仿真系统中，轨道系统动态行为仿真的逼真性和实时性问题，提出了一种能准确模拟轨道系统振动行为的有限长轨道单元法，考虑到钢轨以梁的形式参与振动的实质，通过实时建立具有一定垂向、横向和扭转振动形态的钢轨模型来模拟钢轨的振动行为。仿真结果表明，在保证优良的实时性的同时，利用该方法不仅可以建立更加符合实际的轨道模型，而且还能真实地模拟轨道子系统各组成部件的各种特殊振动行为。