具有深度自适应估计的视觉伺服优化

在手眼机器人视觉伺服中,如何确定机器人末端摄像机移动的速度和对物体的深度进行有效的估计还没有较好的解决方法.本文采用一般模型法,通过求解最优化控制问题来设计摄像机的速度,同时,利用物体初始及期望位置的深度估计值,提出了一种自适应估计的算法对物体的深度进行估计,给出了深度变化趋势,实现了基于图像的定位控制.该方法能够使机器人在工作空间范围内从任一初始位置出发到达期望位置,实现了系统的全局渐近稳定且不需要物体的几何模型及深度的精确值.最后给出的仿真实例表明了本方法的有效性.     

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

3-gated：WLAN中基于负载自适应的动态调度机制

本文针对实时业务在网络负载增加情况下的特点,改进了文[3]的结果,提出了一种新的无线网络MAC层动态调度机制:3-gated调度算法.该机制实现简单,并且可以在不改变现有协议构架甚至不增加开销的基础上较好解决实时业务在网络负载加大环境下用户时延QoS不能得到满足的问题.运用嵌入式马尔可夫链理论和多维概率母函数的方法对该算法进行建模,得到了改进后网络队列缓存的平均排队队长和信息分组平均时延理论解析式.通过仿真验证了理论分析的有效性,表明3-gated算法性能十分接近对称业务最优算法--完全服务调度算法,并避免了后者公平性差的弊端.     

Improved sparsity adaptive matching pursuit algorithm based on compressed sensing

Traditional greedy algorithms need to know the sparsity of the signal in advance, while the sparsity adaptive matching pursuit algorithm avoids this problem at the expense of computational time. To overcome these problems, this paper proposes a variable step size sparsity adaptive matching pursuit (SAMPVSS). In terms of how to select atoms, this algorithm constructs a set of candidate atoms by calculating the correlation between the measurement matrix and the residual and selects the atom most related to the residual. In determining the number of atoms to be selected each time, the algorithm introduces an exponential function. At the beginning of the iteration, a larger step is used to estimate the sparsity of the signal. In the latter part of the iteration, the step size is set to one to improve the accuracy of reconstruction. The simulation results show that the proposed algorithm has good reconstruction effects on both one-dimensional and two-dimensional signals.     

Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.     

Web service adaptation: A decade’s overview

With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture, in which all aspects of monitoring and adaptation (e.g., proactive, cross-layer, and autonomic adaptation) can be expressed. In addition, the emergence of new computing environments to host and execute various types of services (Web/cloud services, big data-intensive services, mobile services, microservices, etc.) raises the need for more efficient monitoring and adaptation systems. This survey aims to bring a synthesis and a road-map to the adaptation of service-based systems. We also discuss adaptation solutions in emerging service models, such as cloud services and big services. Based on an adaptation taxonomy which we extracted from the surveyed approaches, and by identifying the main requirements and goals of service adaptation in Web, cloud and big data environments, detailed analysis and discussions, as well as the open issues, are provided.     

MAML2: meta reinforcement learning via meta-learning for task categories

Meta-learning has been widely applied to solving few-shot reinforcement learning problems, where we hope to obtain an agent that can learn quickly in a new task. However, these algorithms often ignore some isolated tasks in pursuit of the average performance, which may result in negative adaptation in these isolated tasks, and they usually need sufficient learning in a stationary task distribution. In this paper, our algorithm presents a hierarchical framework of double meta-learning, and the whole framework includes classification, meta-learning, and re-adaptation. Firstly, in the classification process, we classify tasks into several task subsets, considered as some categories of tasks, by learned parameters of each task, which can separate out some isolated tasks thereafter. Secondly, in the meta-learning process, we learn category parameters in all subsets via meta-learning. Simultaneously, based on the gradient of each category parameter in each subset, we use meta-learning again to learn a new meta-parameter related to the whole task set, which can be used as an initial parameter for the new task. Finally, in the re-adaption process, we adapt the parameter of the new task with two steps, by the meta-parameter and the appropriate category parameter successively. Experimentally, we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set. Additionally, our algorithm presents a more rapid adaptation process within re-adaptation. Moreover, we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.     

货运列车自适应模型平滑切换及节能优化研究

为了使货运列车运行过程切近实际,提出了一种基于加速度自适应的货运列车质点模型平滑切换方法。首先,建立列车的单质点和多质点模型,基于扰动加速度偏差变化率进行列车多模型的自适应切换研究。然后,为了降低大功率机车牵引电机的工作能耗,引入货运列车牵引控制的多目标和约束条件,建立了多目标优化模型,并提出一种新型的多目标飞蛾扑火(MOMFO)算法用于优化货运列车运行过程。最后,采用HXD1型电力机车牵引50节C80货车作为研究对象,通过仿真验证了所提方法对货运列车牵引电机节能及运行过程优化具有重要意义。     

Two is better than one: A diploid genotype for neural networks

In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.     

自适应性呼叫中心

重点介绍自适应性应答及路由机制的设计与实现.自适应性应答及路由机制在呼叫系统中用来管理整个呼叫流程.该机制将呼叫一开始就提供给用户个性化的自动语音应答环境与后续的自适应性人工应答环境完美地组合在一个完整的工作流程中.自适应性应答及路由机制根据不同的主叫用户属性和话务员属性,将提供个性化的自动语音应答内容和最优的人工话务员应答选择,从而使整个呼叫应答工作流程更为人性化.在实现的机制方面,通过结合两种适应性模型:用户模型和域模型,提供给用户一个高度个性化的应答交互体验.