新的动态环境中目标跟踪和避障的控制方法

针对传感器范围有限的质点移动机器人,提出了一种新的被动式目标跟踪控制方法,使其能在动态变化的环境中有效地避开静止和运动的障碍物。建立了多目标控制问题模型,为环境中的每一个目标生成人工势场,在此基础上设计生成综合控制量。与标准的势场法不同的是：提出的综合势场法是时变的,而且有效地考虑了非静止障碍物和机器人的运动。其基本条件和主要特性均由严格的李亚普洛夫方法导出和证明。仿真实例介绍了该方法的设计过程,通过实验证明了其有效性。     

Variable-dimensional Flower Pollination obstacle avoidance algorithm on autonomous walking of NAO robot in dynamic environment

This paper proposes a novel dynamic obstacle avoidance method based on the Variable-dimensional Flower Pollination (VFP) algorithm, which can avoid dynamic obstacles under an unknown environment. The grid map is used to describe the environment and the shearing map refreshment strategy is used to improve refreshment efficiency. The fitness function is designed by combining Chebyshev distance with Euclidean distance, which can reasonably evaluate the planned path. Simulation results with traditional Flower Pollination algorithm and VFP algorithm are compared. The simulation results show that the VFP algorithm has a faster learning speed than the traditional Flower Pollination algorithm. To verify the simulation result, the VFP algorithm is implemented on the NAO robot, and the experiment result demonstrates that the Variable-dimensional Flower Pollination algorithm is feasible and effective.     

Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds

With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model.     

Tool selection-embedded optimal assembly planning in a dynamic manufacturing environment

This paper presents an approach for tool selection-embedded optimal assembly planning in dynamic manufacturing environments. It aims to embed assembly tools into the planning process of assembly sequences in a dynamic shop-floor. The experimental results demonstrate that the developed approach is efficient and practical for a high fidelity assembly sequence with alternatives of assembly-tool sets. The dynamic assembly planning can efficiently support product assembly by generating feasible assembly sequences. It provides an effective design-aiding tool to virtually deal with various what-if scenarios regarding product assembly. In particular, the Web-based application developed in this research can be incorporated into a high-performance design and manufacturing environment on the Web, forming a distributed, collaborative and globally networked tool for product assembly planning.     

基于云环境下新产品开发团队优选研究

针对云环境下新产品开发团队优选问题,充分考虑团队的研发能力、协调能力和服务质量,建立了包含知识相似度评价模型、协同效应评价模型和服务质量评价模型的综合评价模型;对算法的适应度函数和搜索方式加以改进,提出了改进的人工蜂群算法,对所建立的模型进行求解,为服务需求方选择出最优团队组合;最后,以自动引导运输车（AGV）的新产品开发为例,通过模型求解和算法对比,验证了所提方法的可行性和有效性。     

热电厂优化选址研究

热电厂合理选址将直接关系到电厂的经济运行和可持续发展。在构建热电厂选址评价指标体系的基础上,利用粗糙集信息熵和属性重要度方法获得完全信息驱动的指标权重,通过专家群决策将定量和定性信息相结合,得出热电厂选址优先次序。建立热电厂优化选址的多目标混合整数规划模型,充分考虑建厂的经济性和平均综合效益,在计及决策者偏好和满足现实热负荷需求的情况下,实现了建厂成本最少化和平均综合效益最大化。算例表明所提出的选址方法具有一定的科学性和实用性。     

基于传感器信息的水下机器人动态避障研究

提出一种新的基于传感器信息的自治式水下机器人(AUV)动态避障方法。介绍了传感器的工作原理。通过栅格法把传感器采集的AUV运行环境障碍信息进行合理描述,并预测动态障碍物的速度,保证AUV能够根据传感器信息躲避障碍物,达到航行要求。最后,通过仿真实验对机器人自主避障能力进行了验证。     

基于动态目标位置的智能车辆动态避障控制研究

为了真实地模拟驾驶员在动态环境中避让动态障碍物的行为方式,提出了动态目标位置概念,并采用三次样条曲线作为动态避障的路径拟合曲线。以模糊逻辑为控制策略,以T-S模糊模型为控制结构,以自适应神经网络为隶属度函数的参数调整手段,设计出一种智能车辆横向运动控制器,并通过计算机仿真实现。结果表明,基于动态目标位置概念的控制器设计具有较好的控制性能,较为理想地模拟实际交通环境中车辆动态避障的特性。     

Virtual machine selection and placement for dynamic consolidation in Cloud computing environment

Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to reduce the energy consumption and improve physical resource utilization. Determining which VMs should be migrated from an overloaded host directly influences the VM migration time and increases energy consumption for the whole data center, and can cause the service level of agreement (SLA), delivered by providers and users, to be violated. So when designing a VM selection policy, we not only consider CPU utilization, but also define a variable that represents the degree of resource satisfaction to select the VMs. In addition, we propose a novel VM placement policy that prefers placing a migratable VM on a host that has the minimum correlation coefficient. The bigger correlation coefficient a host has, the greater the influence will be on VMs located on that host after the migration. Using CloudSim, we run simulations whose results let draw us to conclude that the policies we propose in this paper perform better than existing policies in terms of energy consumption, VM migration time, and SLA violation percentage.     

Combustion-enabled underwater vehicles (CUVs) in dynamic fluid environment

Underwater vehicles have opened a unique path to multifunctionality and environmental adaptability. However, inadequate studies have been conducted to investigate the dynamic principle and performance of underwater vehicles in real applications with complex external conditions. Here, we propose a type of combustion-enabled underwater vehicles that can perform stable high-speed motions under dynamic fluid environment. Experiments are conducted to test the kinematic performance. Numerical simulations are developed to investigate the fluid–solid interaction phenomenon, and theoretical modeling is derived to study the dynamic principle of the combustion actuation process. The experimental, numerical, and theoretical results are compared with satisfactory agreements. The underwater vehicles perform ~3.4 body-length distance within 0.2 s and a maximum speed of ~30 body-length per second in horizontal direction. Parametric studies are conducted to investigate the sensitivity of the key factors to the kinematic performance of the reported underwater vehicles. In the end, we report the hybrid combustion-enabled underwater vehicles (CUVs) that combined with propeller to realize continuous driving for multi-mode operations. The experimental, numerical, and theoretical results indicate that hybrid CUVs can achieve more flexible and controllable motion performance.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Research on an optimal selection method for sensor network node under high-speed mobile environment

The traditional selection methods for the selection of sensor network node under high-speed mobile environment is randomness, which cannot effectively use the multiple attributes of nodes, resulting in the irregular distribution of cluster head of nodes, and high energy consumption. An optimal selection method for sensor network node under the high-speed mobile environment based on EERNFS and Naive Bayesian Networks is proposed. By using EERNFS algorithm within each network intercept / sleep cycle, the sensor network node is made processing under high-speed mobile environment, to ensure the stable local connectivity and consistent collaboration intercepts, and reduces energy consumption. Naive Bayes algorithm is used to make optimization of general Bayesian classification method, and set the parameters. In the two-dimensional area, several sensor nodes are randomly placed, and the known two-dimensional area is divided. A certain node is selected arbitrarily in different regions to constitute the original set of Naive Bayes algorithm, and compute the training results and thresholds in the Bayesian system of node set at this moment. On the basis of the threshold, the optimal selection of sensor network node is achieved. Simulation results show that the proposed method not only has higher node coverage, but also the operating efficiency and energy consumption are better than the genetic method.     

未知环境下多AUV协同避障方法研究

针对多AUV（autonomous underwater vehicle）系统在未知环境中进行路径规划时难以兼顾避障与编队的问题,提出了一种基于领航—跟随者与行为的多AUV协同避障方法。首先,通过构造碰撞危险度及偏离目标评价函数,设计了AUV局部路径规划方法;在此基础上,结合编队控制方法,分别为领航者和跟随者设计不同的行为以及行为选择模式。半物理仿真实验结果表明,该算法能够实现多AUV系统在未知环境中的协同避障,且队形偏离度与恢复队形时间优于传统多机器人避障算法。实验结果证明了该算法的可行性与有效性。     

动态字典破解用户口令与安全口令选择

口令认证一直是最主要的身份认证方式。考虑到口令要满足口令策略和易记忆的要求,用户常常会将个人信息组合起来作为口令。因此,为了调查此类口令的比例,以2011年泄露的四种真实口令集为实验素材,预先设定口令的组合结构和格式,使用程序统计使用个人信息组合作为口令的比例。实验结果表明,使用姓名、电话号码、特殊日期等信息组合而成的口令比例为12.41%~25.53%。根据这一规律,提出了动态字典攻击。攻击者可以在获得用户部分个人信息后,生成具有针对性的动态字词典,并以此来破解用户口令。最后,还讨论了如何选择口令以防止攻击者通过动态字典破解用户口令。     

Feature selection using max dynamic relevancy and min redundancy

Pattern Analysis and Applications - Feature selection algorithms based on three-way interaction information have been widely studied. However, most of these traditional algorithms only consider...     

Behavior monitoring under uncertainty using Bayesian surprise and optimal action selection

The increasing trend towards delegating tasks to autonomous artificial agents in safety–critical socio-technical systems makes monitoring an action selection policy of paramount importance. Agent behavior monitoring may profit from a stochastic specification of an optimal policy under uncertainty. A probabilistic monitoring approach is proposed to assess if an agent behavior (or policy) respects its specification. The desired policy is modeled by a prior distribution for state transitions in an optimally-controlled stochastic process. Bayesian surprise is defined as the Kullback–Leibler divergence between the state transition distribution for the observed behavior and the distribution for optimal action selection. To provide a sensitive on-line estimation of Bayesian surprise with small samples twin Gaussian processes are used. Timely detection of a deviant behavior or anomaly in an artificial pancreas highlights the sensitivity of Bayesian surprise to a meaningful discrepancy regarding the stochastic optimal policy when there exist excessive glycemic variability, sensor errors, controller ill-tuning and infusion pump malfunctioning. To reject outliers and leave out redundant information, on-line sparsification of data streams is proposed.     

Dynamic defocus and occlusion compensation of projected imagery by model-based optimal projector selection in multi-projection environment

This paper presents a novel model-based approach of dynamic defocus and occlusion compensation method in a multi-projection environment. Conventional defocus compensation research applies appearance-based method, which needs a point spread function (PSF) calibration when either position or orientation of an object to be projected is changed, thus cannot be applied to interactive applications in which the object dynamically moves. On the other hand, we propose a model-based method in which PSF and geometric calibrations are required only once in advance, and projector’s PSF is computed online based on geometric relationship between the projector and the object without any additional calibrations. We propose to distinguish the oblique blur (loss of high-spatial-frequency components according to the incidence angle of the projection light) from the defocus blur and to introduce it to the PSF computation. For each part of the object surfaces, we select an optimal projector that preserves the largest amount of high-spatial-frequency components of the original image to realize defocus-free projection. The geometric relationship can also be used to eliminate the cast shadows of the projection images in multi-projection environment. Our method is particularly useful in the interactive systems because the movement of the object (consequently geometric relationship between each projector and the object) is usually measured by an attached tracking sensor. This paper describes details about the proposed approach and a prototype implementation. We performed two proof-of-concept experiments to show the feasibility of our approach.     

虚拟环境中优化的OBB碰撞检测算法研究

碰撞检测是计算机图形学的研究热点之一,基于包围盒的碰撞检测算法是一种比较有效的碰撞检测算法.OBB紧密性较好,能有效提高系统的检测效率,但是相交测试的代价太大,根据AABB易于构造、计算简单但紧密性差的特点,提出并实现了优化的OBB碰撞检测算法.实验结果证明:优化算法能够较大幅度地提高了碰撞检测的实时性能,取得较好的效果.     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

Sorting and selection on dynamic data

We formulate and study a new computational model for dynamic data. In this model, the data changes gradually and the goal of an algorithm is to compute the solution to some problem on the data at each time step, under the constraint that it only has limited access to the data each time. As the data is constantly changing and the algorithm might be unaware of these changes, it cannot be expected to always output the exact right solution; we are interested in algorithms that guarantee to output an approximate solution. In particular, we focus on the fundamental problems of sorting and selection, where the true ordering of the elements changes slowly. We provide algorithms with performance close to the optimal in expectation and with high probability.