An inverse kinematics architecture enforcing an arbitrary number of strict priority levels

An efficient inverse kinematics solver is a key element in applications targeting the on-line or off-line postural control of complex articulated figures. In the present paper we progressively describe the strategic components of a very general and robust inverse kinematics architecture. We then present an efficient recursive algorithm enforcing an arbitrary number of strict priorities to arbitrate the fulfillment of conflicting constraints. Due to its local nature, the moderate cost of the solution allows this architecture to run within an interactive environment. The algorithm is illustrated on the postural control of complex articulated figures.     

AutoBrief: a multimedia presentation system for assisting data analysis

We present an approach to generating multimedia presentations that integrates hierarchical planning to achieve communicative goals, and task-based graphic design. A planning process decomposes domain-specific goals to domain-independent goals, which in turn are realized by media-specific techniques such as task-based graphic design. We apply our approach to developing AutoBrief, a system that summarizes large data sets using natural language and information graphics. Finally, we analyze AutoBrief in terms of the standard reference model (SRM).     

Accuracy-based phonological awareness tasks: Are they reliable, efficient, and sensitive to growth?

This two-experiment study examined the efficiency and sensitivity of five accuracy-based phonological awareness tasks for monitoring the development of these skills in kindergarten and Grade 1 students. The first experiment examined responses to different numbers and types of items included in each phonological awareness task for their correspondence to responses obtained from a larger, more inclusive item pool. Results suggested that an internally consistent and valid measure of each skill included 10 items per task, each representing a different linguistic combination. The second experiment examined the interscorer reliability and concurrent validity of the 5 measures, and compared their sensitivity to growth. Sensitivity was examined by administering 12 alternate forms of the tasks once per week to 32 kindergarten and 35 Grade 1 students. Mean slopes computed for each task suggested positive growth across all tasks and grades. Mean kindergarten slopes were significantly steeper than mean Grade 1 slopes for each of the 5 tasks, whereas the most sensitive task for both kindergarten and grade I students was Segmentation. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Cooperation Protocols in Multi-Agent Robotic Systems

Multi-agent robotic systems are useful in many practical applications. For some tasks, such as holding a conference, cooperation among agents are necessary. For other tasks, such as cleaning a room, multiple agents can work in parallel for better performance. This paper provides help-based (HCP) and coordination-based (CCP) protocols for controlling agents to accomplish multi-agent tasks. The HCP utilizes efficient negotiation to coordinate agents into groups. The CCP improves overall performance by exchanging local knowledge among agents and making decisions in parallel. A reactive and modularized agent architecture was employed to implement the protocols. Since each protocol is embedded into the architecture, it is efficient and effective. In addition, the protocols are deadlock-free. The protocols were utilized to solve the Object-Sorting Task, which abstracts two characteristics of tasks: parallelism and cooperation. The experimental results showed that 1) both HCP and CCP are stable under different workload; 2) the protocols can effectively utilize the agent-power to achieve super-linear improvement; 3) The CCP is better than the HCP in both performance and speedup.     

Reinforcement Learning and Robust Control for Robot Compliance Tasks

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.     

FORTH后台任务——时钟实用程序的改进

本文概述了FORTH多任务系统的使用规则和介绍了LMI公司PC/FORTH 2.0版本中的后台时钟任务的改进,避免了运行中对打印输出,屏幕编缉和汉字图形模式的干扰。     

任务内容选择与创造性绩效关系的情景实验研究

以82名大学生为被试，采用情景模拟实验方法，以是否能够选择任务内容作为对个体自主需要满足状况的干预，探讨自主需要满足的程度对个体创造性绩效的影响。结果发现获得选择活动内容机会的被试，自主需要得到更多满足，反映出更高水平的内部动机，产生更高水平的创造性，在一定程度上证明了有机辩证元理论关于动机中介的思想在创造性研究领域中的真实性。     

基于遗传算法的片上多处理器任务调度策略研究

针对在片上多处理器上任务调度的特征,提出了一种基于立体编码的遗传算法。采用适合片上多处理器结构的三维立体编码,使计算节点之间的距离得到了描述,结合提出的个体适应度函数计算公式,使通信代价较小的调度策略优先得到选择,从而通过减少因结点间距离产生的通信开销降低了总的任务完成时间。此外,对算法结构、遗传算子进行了改进提出了内部二维交叉算子,加速了算法的收敛。仿真实验结果表明立体编码的遗传算法能够有效地对任务进行分配和调度,较之传统遗传算法具有较好的性能。     

采用预配置策略的可重构混合任务调度算法

在对可重构硬件资源进行抽象的基础上,采用软硬件混合任务有向无环图来描述应用,提出一种基于列表的混合任务调度算法.该算法通过任务计算就绪顺序及可重构资源状态确定硬件任务的动态预配置优先级,按此优先级进行硬件任务预配置,隐藏硬件任务的配置时间,从而获得硬件任务运算加速.实验结果表明,针对可重构系统中的软硬件混合任务调度,能够有效地降低配置时间对应用执行时间的影响.     

异构分布式系统中实时周期任务的容错调度算法

提出一个基于抢占性实时周期任务的可靠性调度模型,该模型与现有可靠性模型相比充分考虑了单处理机故障容错情况下的系统可靠性,因而更加接近现实和精确.在此基础上,提出一个基于异构分布式系统的实时容错调度算法IRDFTAHS,IRDFTAHS算法以提高系统的可靠性为目标来进行任务的分配,从而在不增加硬件代价的前提条件下通过调度增加了系统的可靠性.该算法同时支持主动和被动两种方式的副版本,使得容错调度算法具有更大的灵活性.最后,通过仿真实验对IRDFTAHS和现有的调度算法在几个方面进行比较.实验结果表明,IRDFTAHS算法的综合性能优于现有算法.