A function-to-task process model for adaptive automation system design

Adaptive automation systems allow the user to complete a task seamlessly with a computer performing tasks at which the human operator struggles. Unlike traditional systems that allocate functions to either the human or the machine, adaptive automation varies the allocation of functions during system operation. Creating these systems requires designers to consider issues not present during static system development. To assist in adaptive automation system design, this paper presents the concept of inherent tasks and takes advantage of this concept to create the function-to-task design process model. This process model helps the designer determine how to allocate functions to the human, machine, or dynamically between the two. An illustration of the process demonstrates the potential complexity within adaptive automation systems and how the process model aids in understanding this complexity during early stage design.     

MoNoC: A monitored network on chip with path adaptation mechanism

Complex systems on chip containing dozens of processing resources with critical communication requirements usually rely on the use of networks on chip (NoCs) as communication infrastructure. NoCs provide significant advantages over simpler infrastructures such as shared busses or point to point communication, including higher scalability, more efficient energy management, higher bandwidth and lower average latency. Applications running on NoCs with more than 10% of bandwidth usage attest that the most significant portion of message latencies refers to buffered packets waiting to enter the NoC, whereas the latency portion that depends on the packet traversing the NoC is sometimes negligible. This work presents an adaptive routing architecture, named Monitored NoC (MoNoC), which is based on a traffic monitoring mechanism and the exchange of high priority control packets. This method enables to adapt paths by choosing less congested routes. Practical experiments show that the proposed path adaptation is a fast process, enabling to transmit packets with smaller latencies, up to 9 times smaller, by using non-congested NoC regions.     

移动环境下一种缓存管理策略

移动计算环境中带宽具有非对称性,并且移动设备资源有限,传统的缓存管理策略需要占用大量的设备计算资源和网络带宽,有效的缓存管理技术能够极大提高移动计算的性能。对此提出一系列的针对移动计算的缓存管理策略。首先根据用户访问频率建立对应的具有生命周期的数据项缓存,然后使用自适应一致性维护策略维护缓存的一致性。通过实验分析,提出的管理策略能够很好地降低带宽占用,并且有效地减少对移动客户端的资源需求。     

基于模块化模型的自适应预失真技术

系统地比较和分析了OFDM传输系统里高功率放大器（HPA）预失真方法的优缺点,提出一种基于模块化模型的有记忆高功率放大器自适应数字预失真方法,该方法分别基于Wiener、Hammerstein和Wiener-Hammerstein模型各自构成一个有记忆HPA的自适应预失真器。从而避免了因分别估计Wiener-Hammerstein模型各个环节特性参数所带来的复杂性。使得有记忆高功率放大器的预失真设计变得简单了。     

混沌和超混沌系统的自适应有限时间投影同步

在混沌系统投影同步的研究中,针对驱动-响应系统达到投影同步的时间不确定问题,提出了一种可以预先计算出同步时间,且在此时间内混沌系统实现投影同步的方法.对给定的驱动系统,构造合适的响应系统;采用自适应控制法,仅在误差系统的部分状态变量上加入自适应控制器;采用李亚普诺夫稳定性理论证明了在所采用的自适应控制律作用下,误差系统稳定同时驱动-响应系统达到有限时间投影同步.以典型的统一混沌系统和超混沌系统进行了数值仿真,结果进一步验证了上述控制方法的有效性与可行性.     

一种基于动态阈值的自适应中值滤波方法

针对图像受到电磁干扰带来的脉冲噪声,对自适应中值滤波方法进行改进。在去除噪声的过程中为了更好地将信号和噪声进行区分开,引入噪声点的预判机制。为满足改进算法对自适应中值滤波半径和窗口的变化的适应．采用自适应动态阈值。通过与SMF、RAMF等方法的比较实验表明,该算法具有较高的峰值信噪比和边缘保持性能。     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.     

基于三维场景和图像边缘特征的浅浮雕建模

针对数字浅浮雕设计,通过对已有的浅浮雕建模方法进行分析,提出一种结合深度场边缘特征自动生成浅浮雕的方法。从三维场景中获取深度图,再将深度图分割成块,利用局部图像结构的优点,用随机结构化森林来学习精确且计算有效的边缘检测器,得到的边缘图再进行处理得到平滑且细节增强的浅浮雕轮廓;视边缘连续图为概率场,提出根据概率场对深度场的梯度域进行自适应的非线性压缩;通过求解泊松方程来恢复浅浮雕的高度场。实验结果表明,该方法对于大部分三维场景能在合理时间内自动生成效果良好的浅浮雕模型。     

基于万有引力的自适应近邻传播聚类算法

针对近邻传播（AP）聚类算法对参数偏向参数（Preference）敏感、不适用于稀疏数据、聚类结果中会出现错误聚类的样本点的问题,提出基于万有引力的自适应近邻传播聚类（GA-AP）算法。首先,在传统AP算法的基础上采用引力搜索机制对样本进行全局寻优;其次,在全局寻优的基础上利用信息熵和自适应增强（AdaBoost）算法找到每个簇内正确聚类和错误聚类的样本点,并计算出这些样本点的权值,用计算出的权值更新对应的样本点,从而更新相似度、Preference取值、吸引度和隶属度,并进行重新聚类。不断操作以上步骤直到达到最大的迭代次数。通过在9个数据集上的仿真实验得出,相比于基于自适应属性加权的近邻传播聚类（AFW_AP）算法、AP算法、K均值聚类（K-means）算法和模糊C均值（FCM）算法,所提算法的纯度（Purity）、F值（F-measure）和准确率（ACC）的平均值分别最高提升了0.69、71.74%和98.5%。实验结果表明,所提算法降低了对偏向参数的依赖,提高了聚类效果,特别是对于稀疏数据集的聚类结果的准确率。