Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

An immune based two-phase approach for the multiple-type surveillance camera location problem

For the recent years, there are more and more surveillance cameras set on lanes, train/bus stations, hospitals, schools, banks, supermarkets, shopping malls, etc., to improve the safety of people around them. In Taiwan, the so-called “E-Patrol” system is used to support policemen for providing patrol services and several criminal cases have been solved with the use of surveillance cameras of “E-Patrol” system. It is well known that inappropriate settings of surveillance cameras will result in some dead angles and oversetting of surveillance cameras will waste limited resources. The setting of surveillance cameras is an important issue and a complicated NP problem. In this paper, we consider the design problem of multiple-type surveillance cameras on various lanes in which two kinds of variables have to be decided simultaneously, namely, discrete variables for types of surveillance cameras and continuous variables for locations of surveillance cameras. The main purposes of this paper are (i) to propose an immune based two-phase approach for solving the optimal design of multiple-type surveillance camera problem, and (ii) to show the excellent performance of the proposed two-phase approach.     

Surveillance camera scheduling: a virtual vision approach

We present a surveillance system, comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom (PTZ) active cameras, which automatically captures high-resolution videos of pedestrians as they move through a designated area. A wide-FOV static camera can track multiple pedestrians, while any PTZ active camera can capture high-quality videos of one pedestrian at a time. We formulate the multi-camera control strategy as an online scheduling problem and propose a solution that combines the information gathered by the wide-FOV cameras with weighted round-robin scheduling to guide the available PTZ cameras, such that each pedestrian is observed by at least one PTZ camera while in the designated area. A centerpiece of our work is the development and testing of experimental surveillance systems within a visually and behaviorally realistic virtual environment simulator. The simulator is valuable as our research would be more or less infeasible in the real world given the impediments to deploying and experimenting with appropriately complex camera sensor networks in large public spaces. In particular, we demonstrate our surveillance system in a virtual train station environment populated by autonomous, lifelike virtual pedestrians, wherein easily reconfigurable virtual cameras generate synthetic video feeds. The video streams emulate those generated by real surveillance cameras monitoring richly populated public spaces.A preliminary version of this paper appeared as [1].     

Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.     

A Multi-Camera Active-Vision System for Deformable-Object-Motion Capture

A novel methodology is proposed to select the on-line, near-optimal positions and orientations of a set of dynamic cameras, for a reconfigurable multi-camera active-vision system to capture the motion of a deformable object. The active-vision system accounts for the deformation of the object-of-interest by fusing tracked vertices on its surface with those triangulated from features detected in each camera’s view, in order to predict the shape of the object at subsequent demand instants. It then selects a system configuration that minimizes error in the recovered position of each of these features. The tangible benefits of using a reconfigurable system, particularly with translational cameras, versus systems with static cameras in a fixed configuration, are demonstrated through simulations and experiments in both obstacle-free and obstacle-laden environments.     

An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles

Sliding mode control is a very attractive control scheme because of its robustness against both structured and unstructured uncertainties as well as external disturbances. In this way, it has been widely employed for the dynamic positioning of remotely operated underwater vehicles. Nevertheless, in such situations the discontinuities in the control law must be smoothed out to avoid the undesirable chattering effects. The adoption of properly designed boundary layers has proven effective in completely eliminating chattering, however, leading to an inferior tracking performance. This work describes the development of a dynamic positioning system for remotely operated underwater vehicles. The adopted approach is primarily based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm for uncertainty/disturbance compensation. Using the Lyapunov stability theory and Barbalat’s lemma, the boundedness and convergence properties of the closed-loop signals are analytically proven. The performance of the proposed control scheme is also evaluated by means of numerical simulations.     

Dynamic compensation by fusing a high-speed actuator and high-speed visual feedback with its application to fast peg-and-hole alignment

This paper presents a dynamic compensation concept to grapple with the dynamic defects of a traditional robot arm, especially while performing high-speed endpoint regulations. The proposed high-speed dynamic compensation concept offers a new point of view for cooperating with a traditional manipulator to realize highly dexterous performance of manipulations. The concept is realized through adoption of a high-speed light-weight actuator as well as endpoint closed loop configured high-speed cameras. The dynamic compensation is analyzed experimentally with 1000 Hz visual feedback and a high-speed finger for a robot arm in the case of one degree of freedom. The advantage of the proposed approach is that the modeling for the robot system’s dynamics is not needed, whereas it is necessary and trivial in order to realize high-speed regulations by traditional approaches. Thus, the control issue becomes easier with the proposed approach. As an application for this concept, fast peg-and-hole alignment with large position and attitude uncertainty is studied. The alignment algorithm is based on a visual compliance strategy. Alignment experiments show that with the proposed concept of dynamic compensation as well as visual compliant motion control, robust and fast convergence was realized for most cases.     

Internet of surveillance: a cloud supported large-scale wireless surveillance system

Large-scale video surveillance systems are among the necessities for securing our life these days. The high bandwidth demand and the large storage requirements are the main challenges in such systems. To face these challenges, the system can be deployed as a multi-tier framework that utilizes different technologies. In such a framework, technologies proposed under the umbrella of the Internet of Things (IoT) can play a significant rule in facing the challenges. In video surveillance, the cameras can be considered as “the things” that are streaming videos to a central processing and storage server (the cloud) through the Internet. Wireless technologies can be used to connect wireless cameras to the surveillance system more conveniently than wired cameras. Unfortunately, wireless communication in general tend to have limited bandwidth that needs careful management to achieve scalability. In this paper, we design and evaluate a reliable IoT-based wireless video surveillance system that provides an optimal bandwidth distribution and allocation to minimize the overall surveillance video distortion. We evaluate our system using NS-3 simulation. The results show that the proposed framework fully utilizes the available cloud bandwidth budget and achieves high scalability.     

一种基于特征匹配的目标入侵检测方法

视频监控中常用云台摄像机监控视场较大的区域.对于云台摄像机跟随拍摄的情况,提出了一种基于特征匹配的目标入侵检测方法.通过提取的尺度不变特征变换(SIFT)特征点对,将当前图像和全景图像进行匹配,从而得到当前图像和全景图像投影关系,再将当前图像的坐标系变换到全景图像下,最后运用差分法,找到入侵目标.实验结果表明,即使当前图像与全景图像存在尺度、缩放、形变等差异,通过本方法也可正确地检测出入侵目标.     

一种非重叠多摄像头的实时监控系统

大范围场景的监控需要使用多个摄像头。论文利用运动目标的颜色信息和路径特征,设计了一种非重叠多摄像头的实时监控系统。系统采用分布式多层次结构,在进行单摄像头层的处理时,根据像素点亮度变化检测和跟踪运动目标,同时获取运动目标的外形信息和路径特征;在进行多摄像头层的处理时,使用估计目标外形变化和建立路径模型方法融合多个摄像头信息,实现目标在非重叠多摄像头的跟踪。该系统不要求校准摄像头,也不要求建立完整的场景模型,即便在有亮度变化的环境中,仍能立即准确跟踪目标。实验证明提出的方法有好的跟踪效果。     

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.

     

People counting via multiple views using a fast information fusion approach

Real-time estimates of a crowd size is a central task in civilian surveillance. In this paper we present a novel system counting people in a crowd scene with overlapping cameras. This system fuses all single view foreground information to localize each person present on the scene. The purpose of our fusion strategy is to use the foreground pixels of each single views to improve real-time objects association between each camera of the network. The foreground pixels are obtained by using an algorithm based on codebook. In this work, we aggregate the resulting silhouettes over cameras network, and compute a planar homography projection of each camera’s visual hull into ground plane. The visual hull is obtained by finding the convex hull of the foreground pixels. After the projection into the ground plane, we fuse the obtained polygons by using the geometric properties of the scene and on the quality of each camera detection. We also suggest a region-based approach tracking strategy which keeps track of people movements and of their identities along time, also enabling tolerance to occasional misdetections. This tracking strategy is implemented on the result of the views fusion and allows to estimate the crowd size dependently on each frame. Assessment of experiments using public datasets proposed for the evaluation of counting people system demonstrates the performance of our fusion approach. These results prove that the fusion strategy can run in real-time and is efficient for making data association. We also prove that the combination of our fusion approach and the proposed tracking improve the people counting.

     

监控系统中的多摄像头协同算法

给出了一个分布式多摄像头监控系统结构,描述了一种使用路径模型和目标外形变化估计的数据融合方案,提出了一种新的在分布式监控系统中的多摄像头协同算法(PDA,PriorityandDistanceAlgorithm)。提出的算法基于多摄像头数据融合结果,根据任务优先级、目标与摄像头之间的距离及目标的可见性情况,分配摄像头给目标,其特点是使系统中有高优先级并距离摄像头最近的可见目标优先分配摄像头。实验结果表明提出算法能协同多摄像头可靠地跟踪人。     

Dynamic positioning of remotely operated underwater vehicles

The paper describes an automatic dynamic positioning system for remotely operated underwater vehicles (ROVs) using a mechanical passive arm for position measurement that is suitable for inspection and intervention tasks requiring precise positioning. Good dynamic performance in tracking was also attained, particularly with the variable structure model reference adaptive control strategy     

Architecture layers and engineering approach for agent-based system

It is necessary to support user-centric service provision paradigm in distributed, dynamic and complex computing environment. Software agent technology is considered as one of the technologies suitable to adopt such computing environment. Many researchers have emphasized on agent-based system development, but, many agent-based systems are designed and constructed in ad hoc. In particular, they do not enough consider system organization and performance aspects. More systematic engineering approach of agent-based system is required. We propose the layered architecture and engineering approach for agent-based system design. We devise the layers necessary to design agent-based system, and methods to engineer each layer. Also we show that the devised approach can be used to design agent-based system and analyze system features. The layered architecture and engineering approach of agent-based system proposed in this paper support that engineer designs efficient agent-based system.     

基于单目视觉的水下机器人悬停定位技术与实现

以7000m载人潜水器的水下悬停为应用背景,以单目CCD摄像机为传感器,提出了基于模型的单目视觉定位和基于特征的视觉伺服两种水下机器人视觉悬停技术方案,分别适用于已知模型的观察目标和未知模型的观察目标两种情况．以自行研制的水下机器人控制系统实验平台为实验载体,利用前视摄像机和水下人工目标,建立了水下演示实验验证系统,并在室内实验水池中,分别实现了两种悬停方法的水下演示实验．演示实验表明：在两种视觉悬停方法的闭环控制下,水下机器人能够抵抗恒定水流干扰和人工位置扰动,很好地实现水下悬停作业．     

A practical multiple model adaptive strategy for single-loop MPC

This paper details a multiple model adaptive control strategy for model predictive control (MPC). To maintain performance of this linear controller over a wide range of operating levels, a multiple model adaptive control strategy for dynamic matrix control (DMC), the process industry's standard for MPC, is presented. The method of approach is to design multiple linear DMC controllers. The tuning parameters for the linear controllers are obtained using novel analytical expressions. The controller output of the adaptive DMC controller is a weighted average of the multiple linear DMC controllers. The capabilities of the multiple model adaptive strategy for DMC are investigated through computer simulations and an experimental system.     

基于场景模型的多摄像机目标跟踪算法

在多个相机组成的视频监视系统中,当目标物移出某一相机的视野而进入下一个时,如何实现相机的交接,实现目标物的继续跟踪是监视系统中要解决的关键问题。针对该问题,提出了一种基于位置比较的多摄像机运动目标跟踪方法。为获得目标物的位置,建立多个相机与目标物世界坐标之间映射关系的场景模型,并根据目标物出现在不同相机之间的视野边界线上的瞬间时刻的位置来给出重叠视野的边界线。由此可对任意角度摆放的多个具有重叠视野的相机之间运行的目标物进行接力跟踪。该方法可以适应多个目标物同时进入场景的情况,实验结果表明,该方法具有较高的鲁棒性,能够满足视频跟踪的实时性要求。