基于MIPS内核的SoC软硬件协同仿真

针对基于MIPS系列处理器内核的高清电视解码SoC，构建了一个软硬件协同仿真环境。连接MIPS处理器内核的VMC模型和SoC的RTL模型，利用VMC模型支持MIPS指令集的特性运行测试汇编程序，实现了SoC软硬件的同步调试，有效地提高了系统验证的效率。     

一个移动Agent在IP网络流量监控中的应用

介绍了当前IP网络流量监控的主要方法，提出了一种运用移动代理技术来实现对IP网络流量有效监控的方法，阐述了移动代理环境，运用移动代理进行流量监控的过程，以及移动Agent和SNMPAgent的交互操作。     

VMC0656mu高速加工机床的开发

介绍了VMC0656mu高速加工机床在开发过程中的几个关键技术点.     

VMC600主运动系统空运转试验装置的设计

描述了VMC600加工中心仿真型主运动系统空运转试验装置的设计、安装及应用验证,解决了在整机上做此试验操作不便也不经济的问题,实现了既确保主运动技术指标有一定的储备量,又很方便地试验和快速调整,一次交检合格率达100%的目标。     

Weather display symbology affects pilot behavior and decision-making

ObjectiveTo examine the basic discriminability of weather display symbols and to assess the effects of weather display symbology on pilot behavior and decision-making.MethodDuring a cockpit simulation, 24 instrument-rated general aviation (GA) pilots were randomly allocated to one of three simulation groups. Pilots flew a Cessna 172 single-engine GA aircraft under Visual Meteorological Conditions (VMC) and Instrument Meteorological Conditions (IMC) while avoiding hazardous weather. We manipulated the weather display so that each pilot group used a different weather symbology (i.e., symbols and colors). We measured dependent variables for weather avoidance, communication, weather display usage, and cognitive engagement (i.e., oxygenation from Functional Near-Infrared [fNIR] measures). During a change-detection experiment, 20 naïve participants performed a detection task of changes in Aviation Routine Weather Reports (METARs), precipitation, Significant Meteorological Information (SIGMET), lightning, and time-stamp images.ResultsThe simulation outcome showed credible pilot group differences in weather deviations, cognitive engagement, and weather display usage. The change-detection experiment revealed credible differences in discriminability of METAR, SIGMET, and lightning symbols.ConclusionSymbol and color variations in weather displays contribute to perceptual asymmetries which affect pilot behavior and decision-making. We recommend the development of cockpit applications that use weather data to automatically track hazardous conditions and alert the pilot of potential weather conflicts or weather changes.     

云数据中心高效的虚拟机整合方法

针对传统虚拟机整合（VMC）方法难以保持主机工作负载长期稳定的问题,提出一种基于高斯混合模型的高效虚拟机整合（GMM-VMC）方法。为了准确地预测主机负载的变化趋势,首先,使用高斯混合模型（GMM）对活动物理主机的工作负载历史记录进行拟合;然后,根据活动物理主机工作负载的GMM和主机自身的资源配置情况计算主机的过载概率,并根据过载概率判定主机是否存在过载风险;对存在过载风险的物理主机,根据部署在该物理主机上的虚拟机对降低主机过载风险的贡献和虚拟机迁移所需的时间这两个指标进行待迁移虚拟机选择;最后,使用GMM估算待迁移虚拟机对各个目标主机过载风险的影响,并选择受影响最小的主机作为目标主机。通过CloudSim仿真平台模拟该GMM-VMC方法,并根据能源消耗、服务质量（QoS）、整合效率等指标与已有的整合方法进行对比,实验结果表明,GMM-VMC方法能够有效地降低数据中心能耗,提高服务质量。     

Hierarchical control of cooperative nonlinear dynamical systems

Cooperative control methods that are scalable with low computational cost are crucial for networked dynamical systems to respond quickly in unknown or cluttered environments. In an attempt to make the problem tractable, many existing cooperative controls are designed with oversimplified assumptions and/or without the capabilities of rapidly handling different environmental and dynamical constraints. In this article, proposed is a two-level hierarchical, cooperative control framework using a divide-and-conquer strategy so that challenges can be separately handled at different levels. It is scalable and has low computational cost. Based on a simplified homogeneous double-integrator dynamic model, the top-level planner first computes cooperative trajectories satisfying obstacle avoidance requirements. Then at the lower level, state and control constraints, nonlinear dynamics and self-collision/obstacle avoidance as related to the real system are addressed through a bio-inspired fast trajectory planning algorithm. The stability of the overall hierarchical structure is proven. Two examples, a differential-drive ground vehicle formation control and an unmanned aerial vehicle formation flight, are used to illustrate the advantages of the proposed hierarchical framework.     

Visual Motor Control of a 7 DOF Robot Manipulator Using Function Decomposition and Sub-Clustering in Configuration Space

This paper deals with real-time implementation of visual-motor control of a 7 degree of freedom (DOF) robot manipulator using self-organized map (SOM) based learning approach. The robot manipulator considered here is a 7 DOF PowerCube manipulator from Amtec Robotics. The primary objective is to reach a target point in the task space using only a single step movement from any arbitrary initial configuration of the robot manipulator. A new clustering algorithm using Kohonen SOM lattice has been proposed that maintains the fidelity of training data. Two different approaches have been proposed to find an inverse kinematic solution without using any orientation feedback. In the first approach, the inverse Jacobian matrices are learnt from the training data using function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. In the second approach, a concept called sub-clustering in configuration space is suggested to provide multiple solutions for the inverse kinematic problem. Redundancy is resolved at position level using several criteria. A redundant manipulator is dexterous owing to the availability of multiple configurations for a given end-effector position. However, existing visual motor coordination schemes provide only one inverse kinematic solution for every target position even when the manipulator is kinematically redundant. Thus, the second approach provides a learning architecture that can capture redundancy from the training data. The training data are generated using explicit kinematic model of the combined robot manipulator and camera configuration. The training is carried out off-line and the trained network is used on-line to compute the joint angle vector to reach a target position in a single step only. The accuracy attained is better than the current state of art.     

VMC600主运动系统空运转试验装置的设计

描述了VMC600仿真型主运动系统空运转试验装置的设计、安装及应用验证,解决了在整机上做此试验操作不便也不经济的问题,实现了既确保主运动技术指标有一定的储备量,又很方便地试验和快速调整,一次交检合格率达100%的目标.     

An energy-aware virtual machines consolidation method for cloud computing: Simulation and verification

Cloud systems have become an essential part of our daily lives owing to various Internet-based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy-aware method for the VMC problem, which is called energy-aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non-functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.