支持混合演示场景的移动学习直播系统

移动学习的网络环境中往往无法保证可靠持久的连接,而且在越来越常见的混合演示场景的编码中传统的屏幕编码技术的输出帧率较低而通用视频编码技术的静态图像质量较差。针对上述两个问题提出的支持混合演示场景的移动课程直播系统,利用HTTP-Streaming技术提升系统对不同网络环境的适应能力,并且使用改进的屏幕编码码率控制算法使得系统在有效控制峰值码率的基础上能够支持教师端屏幕可能出现的混合演示场景的编码。实验证明在当前主流配置的手机上课程内容能够流畅播放。     

Integrating planning and control for single-bodied wheeled mobile robots

This paper presents an approach to couple path planning and control for mobile robot navigation in a hybrid control framework. We build upon an existing hybrid control approach called sequential composition, in which a set of feedback control policies are prescribed on well-defined domains contained in the robot’s free space. Each control policy drives the robot to a goal set, which lies in the domain of a subsequent policy. Control policies are deployed into the free state space so that when composed among one another, the overall action of the set of control policies drives the robot to perform a task, such as moving from a start to a goal location or patrolling a perimeter. A planner determines the sequence of control policies to be invoked. When control policies defined in this framework respect the low-level dynamics and kinematics of the system, this formal approach guarantees that high-level tasks are either accomplished by a given set of policies, or verifies that the tasks are not achievable with the given policies.     

Combining technology roadmap and system dynamics simulation to support scenario-planning: A case of car-sharing service

Due to the volatile market environment, the use of scenario approach comes to the forefront in business strategy. As a means of scenario planning, several approaches have been proposed and conducted. However, previous research, mainly having resorted to the expert judgment for planning and evaluation, still remains conceptual and lacks a systematic link to the planning process. In response, this paper provides an integrative approach to the technology roadmap and system dynamics to support scenario planning. The proposed approach consists of three parts: scenario building, technology roadmapping, and system dynamics simulation. The first step is to construct the scenarios which are used as inputs for the scenario planning. Second, technology roadmap is developed, incorporating the scenarios built in the first step. The technology roadmap works as a strategic framework to realize the hypothetical scenarios, linking the external and hypothetical business and internal strategies. Finally, the strategic model for technology roadmap is transferred to the operational viewpoint using system dynamics. When the simulation ends, the result of each scenario is reflected to the technology roadmapping, making the multi-path technology roadmapping. As an illustrative example, three scenarios of car-sharing business are developed and analyzed.     

Long-term knowledge acquisition using contextual information in a memory-inspired robot architecture

In this paper, we present a novel cognitive framework allowing a robot to form memories of relevant traits of its perceptions and to recall them when necessary. The framework is based on two main principles: on the one hand, we propose an architecture inspired by current knowledge in human memory organisation; on the other hand, we integrate such an architecture with the notion of context, which is used to modulate the knowledge acquisition process when consolidating memories and forming new ones, as well as with the notion of familiarity, which is employed to retrieve proper memories given relevant cues. Although much research has been carried out, which exploits Machine Learning approaches to provide robots with internal models of their environment (including objects and occurring events therein), we argue that such approaches may not be the right direction to follow if a long-term, continuous knowledge acquisition is to be achieved. As a case study scenario, we focus on both robot–environment and human–robot interaction processes. In case of robot–environment interaction, a robot performs pick and place movements using the objects in the workspace, at the same time observing their displacement on a table in front of it, and progressively forms memories defined as relevant cues (e.g. colour, shape or relative position) in a context-aware fashion. As far as human–robot interaction is concerned, the robot can recall specific snapshots representing past events using both sensory information and contextual cues upon request by humans.     

How to Control Robots Interacting with Dynamic Environment

The goal of this paper is to shed light on the control problem of constrained robot motion from the aspect of the dynamical nature of the environment with which the robot is in contact. Therefore, the criticism of traditional hybrid control which allows position/force feedback loops to split into independent control with respect to position and force, is not the main point we want to make. Reference to the papers written by the founders of hybrid control and their numerous followers served only to better understand the reason and motivation for suggesting a different approach to control of robots interacting with environment.The paper has a predominantly review character, based on recently published work. It also contains some new, unpublished results in the framework of the unified approach to the position/force control of robots, proposed by the present author and his co-workers. By pointing to the possibility of introducing an environment dynamics in the contact tasks of the machining type, the author emphasizes that the proposed dynamically interactive control can be applied to a completely different class of tasks, in which a contact is made between the system (constructions or structure) and very specific kinds of dynamic environments.     

Adversary Aware Surveillance Systems

We consider surveillance problems to be a set of system-adversary interaction problems in which an adversary can be modeled as a rational (selfish) agent trying to maximize his utility. We feel that appropriate adversary modeling can provide deep insights into the system performance and also clues for optimizing the system's performance against the adversary. Further, we propose that system designers should exploit the fact that they can impose certain restrictions on the intruders and the way they interact with the system. The system designers can analyze the scenario to determine conditions under which system outperforms the adversaries, and then suitably reengineer the environment under a “scenario engineering” approach to help the system outperform the adversary. We study the proposed enhancements using a game theoretic framework and present results of their adaptation to two significantly different surveillance scenarios. While the precise enforcements for the studied zero-sum ATM lobby monitoring scenario and the nonzero-sum traffic monitoring scenario were different, they lead to some useful generic guidelines for surveillance system designers.      

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

YACS: a new learning classifier system using anticipation

 A new and original trend in the learning classifier system (LCS) framework is focussed on latent learning. These new LCSs call upon classifiers with a (condition), an (action) and an (effect) part. In psychology, latent learning is defined as learning without getting any kind of reward. In the LCS framework, this process is in charge of discovering classifiers which are able to anticipate accurately the consequences of actions under some conditions. Accordingly, the latent learning process builds a model of the dynamics of the environment. This model can be used to improve the policy learning process. This paper describes YACS, a new LCS performing latent learning, and compares it with ACS.     

A computational modeling for real ecosystems based on P systems

In this paper, a P systems based general framework for modeling ecosystems dynamics is presented. Particularly, ecosystems are specified by means of multienvironment P systems composed of a finite number of environments, each of them having an extended P system with active membranes. The semantics is of a probabilistic type and it is implemented by assigning each rule of the system a probabilistic constant which depends on the environment and the run time. As a case study, two real ecosystems are described: scavenger birds in the Catalan Pyrenees and the zebra mussel (Dreissena Polymorpha) in Ribarroja reservoir (Spain).     

多种OOPL的统一框架模型

本文针对已有的面向对象语言，探讨了不同语言编写的混和类及其对象的组装问题，提出了一种具有统一框架的ＯＯＰＬ模型。在该模型中，引入了类构件和消息体的概念；一个类构件由一个类规范和０到多个类实现组成；引入接口相容和接口一致，以方便混和对象之间的消息通信；引入接口继承和实现继承，以刻画混合类构件之间的继承关系；类规范的操作换名接口说明和类实现的接口调整，可以解决类构件间多继承关系引起的名字冲突问题；消息     

The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services

We describe a novel framework for specifying and monitoring Service Level Agreements (SLA) for Web Services. SLA monitoring and enforcement become increasingly important in a Web Service environment where enterprise applications and services rely on services that may be subscribed dynamically and on-demand. For economic and practical reasons, we want an automated provisioning process for both the service itself as well as the SLA managment system that measures and monitors the QoS parameters, checks the agreed-upon service levels, and reports violations to the authorized parties involved in the SLA management process. Our approach to these issues is presented in this paper. The Web Service Level Agreement (WSLA) framework is targeted at defining and monitoring SLAs for Web Services. Although WSLA has been designed for a Web Services environment, it is applicable as well to any inter-domain management scenario, such as business process and service management, or the management of networks, systems and applications in general. The WSLA framework consists of a flexible and extensible language based on XML Schema and a runtime architecture comprising several SLA monitoring services, which may be outsourced to third parties to ensure a maximum of objectivity. WSLA enables service customers and providers to unambiguously define a wide variety of SLAs, specify the SLA parameters and the way they are measured, and relate them to managed resource instrumentations. Upon receipt of an SLA specification, the WSLA monitoring services are automatically configured to enforce the SLA. An implementation of the WSLA framework, termed SLA Compliance Monitor, is publicly available as part of the IBM Web Services Toolkit.     

Hybrid artificial intelligence architecture for diagnosis and decision-making in manufacturing

This paper presents an architecture which combines artificial neural networks (ANNs) and an expert system (ES) into a hybrid, self-improving artificial intelligence (AI) system. The purpose of this project is to explore methods of combining multiple AI technologies into a hybrid intelligent diagnostic and advisory system. ANNs and ESs have different strengths and weaknesses, which can be exploited in such a way that they are complementary to each other: strengths in one system make up for weaknesses in the other, andvice versa. There is, presently, considerable interest in ways to exploit the strengths of these methodologies to produce an intelligent system which is more robust and flexible than one using either technology alone. Any process which involves both data-driven (bottom-up) and concept-driven (top-down) processing is especially well suited to displaying the capabilities of such a hybrid system. The system can take an incoming pattern of signals, as from various points in an automated manufacturing process, and make intelligent process control decisions on the basis of the pattern as preprocessed by the ANNs, with rule-based heuristic help or corroboration from the ES. Patterns of data from the environment which can be classified by either the ES or a human consultant can result in a high-level ANN being created and trained to recognize that pattern on future occurrences. In subsequent cases in which the ANN and the ES fail to agree on a decision concerning the environmental situation, the system can resolve those differences and retrain the networks and/or modify the models of the environment stored in the ES. Work on a hybrid system for perception in machine vision has been funded initially by an Oak Ridge National Laboratory seed grant, and most of the system components are operating presently in a parallel distributed computer environment.     

Execution models for reconfigurable embedded real‐time systems

This paper deals with the verification and assignment into the execution environment of Reconfigurable Control Applications following the Component‐based International Industrial Standard IEC61499. According to this Standard, a Function Block (FB) is an event‐triggered component and an application is an FB network that has to meet temporal properties according to user requirements. If a reconfiguration scenario is applied at run‐time, then the FB network implementing the application is totally changed or modified. To cover all possible cases, we classify such scenarios into three classes and we define an agent‐based architecture designed with nested state machines to automatically handle all possible reconfigurations. To verify and assign Function Blocks corresponding to each reconfiguration scenario into the execution environment, we define an approach based on the exploration of reachability graphs to verify temporal properties. This approach constructs feasible Operating System tasks encoding the FB network that corresponds to each scenario. Therefore, the application is considered as sets of Operating System (OS) tasks where each set is to load in memory when the corresponding reconfiguration scenario is applied by the agent. We developed the tool X ‐Assign supporting these contributions that we apply on the FESTO production system available in our research laboratory. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Efficient Location Updates for Continuous Queries over Moving Objects

The significant overhead related to frequent location updates from moving objects often results in poor performance. As most of the location updates do not affect the query results, the network bandwidth and the battery life of moving objects are wasted. Existing solutions propose lazy updates, but such techniques generally avoid only a small fraction of all unnecessary location updates because of their basic approach (e.g., safe regions, time or distance thresholds). Furthermore, most prior work focuses on a simplified scenario where queries are either static or rarely change their positions. In this study, two novel efficient location update strategies are proposed in a trajectory movement model and an arbitrary movement model, respectively. The first strategy for a trajectory movement environment is the Adaptive Safe Region (ASR) technique that retrieves an adjustable safe region which is continuously reconciled with the surrounding dynamic queries. The communication overhead is reduced in a highly dynamic environment where both queries and data objects change their positions frequently. In addition, we design a framework that supports multiple query types (e.g., range and c-kNN queries). In this framework, our query re-evaluation algorithms take advantage of ASRs and issue location probes only to the affected data objects, without flooding the system with many unnecessary location update requests. The second proposed strategy for an arbitrary movement environment is the Partition-based Lazy Update (PLU, for short) algorithm that elevates this idea further by adopting Location Information Tables (LITs) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. We first define the data structure of an LIT which is essentially packed with a set of surrounding query locations across the terrain and discuss the mobile-side and server-side processes in correspondence to the utilization of LITs. Simulation results confirm that both the ASR and PLU concepts improve scalability and efficiency over existing methods.     

多技术系统基于VHDL-AMS的统一建模与仿真技术研究

机电一体化系统设计要求跨领域的统一建模与仿真，多刚体动力学是其中的一个难点．通常的做法是采用动力学专用软件建模，求得DAEs形式的运动方程后再转化为集成环境中的统一建模语言．首先基于广义基尔霍夫网络理论，提出了基于端口的多技术系统集成组件，并采用VHDL—AMS混合系统建模语言进行描述．为了更有效的集成多刚体动力学，通过选取参考基作为端点，构造了计算多刚体系统动力学的广义基尔霍夫网络模型，具体阐述了基本组件的VHDL-AMS行为模型，从而将多刚体系统纳入基于VHDL—AMS的集成设计和仿真环境中．最后用一个实例进行了具体说明．     

Adaptive Hybrid System Framework for Unified Impedance and Admittance Control

Impedance and Admittance Control are two well-known controllers to accomplish the same goal: the regulation of the mechanical impedance of manipulators interacting dynamically with the environment. However, they both are affected by a strong limitation deriving from their fixed causality, which causes their inability to provide good performance over a large spectrum of environment stiffnesses. In this paper an adaptive hybrid system framework is proposed to unify Impedance and Admittance formulations and consequently overcome this limit. Indeed, the hybrid framework interpolates the opposite performance and stability characteristics of the above-mentioned impedance-based control strategies leading to a family of controllers with intermediate properties, and thus suitable for several conditions. Moreover, the adaptivity allows the hybrid system to operate properly in an environment characterized by unknown and even time-varying stiffness. Especially, the work focuses on the development of this latter aspect and an adaptive solution based on a feedforward Neural Network is presented. The effectiveness of the novel control strategy is demonstrated by means of numerical simulations.     

Speed planning and energymanagement strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.     

Implementation and Provisioning of Federated Networks in Hybrid Clouds

Federated cloud networking is needed to allow the seamless and efficient interconnection of resources distributed among different clouds. This work introduces a new cloud network federation framework for the automatic provision of Layer 2 (L2) and Layer 3 (L3) virtual networks to interconnect geographically distributed cloud infrastructures in a hybrid cloud scenario. This framework provides a simple, secure and elastic solution to create and manage federated networks across heterogeneous cloud providers, with abstraction of the underlying technologies. The paper also analyzes the main technologies and topologies that can used the framework to construct L2 and L3 overlay networks within hybrid clouds. In order to demonstrate the viability of the proposed solution and compare the different topologies, the article shows a proof-of-concept of a real federated network deployment in a hybrid cloud, which spans a local private cloud, managed with OpenNebula, and two public clouds, two different regions of Amazon EC2. Results show that L2 and L3 overlay connectivity can be achieved with a minimal bandwidth overhead, lower than 10%.