Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic

High-Altitude Long-Endurance(HALE) solar-powered Unmanned Aircraft Vehicles(UAVs) can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers. Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day. In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL) framework is...     

基于深度学习的空间站舱内服务机器人视觉跟踪

为提升舱内跟随服务机器人的任务辅助能力,解决机器人对航天员的视觉跟踪问题,提出了一种基于深度学习和概率模型的人体视觉跟踪算法。利用深度卷积神经网络实现了对穿着多样、姿态任意人体的稳定检测。结合人体检测结果,设计了运动预测概率模型,实现了对指定人员准确、连续的跟踪。算法对包含大多数航天员活动的多个数据集进行了验证。实验结果表明:提出的跟踪算法实现了对穿着多样、姿态任意人体的稳定跟踪,并有效避免了由于穿着相似、遮挡可能造成的误跟踪问题。该算法为空间站舱内跟随服务机器人对航天员的视觉跟踪提供了有效的解决方法。算法基于融合的RGB-D图像,工程上易于构建和实现,也可拓展到其他跟随服务机器人视觉跟踪任务中。     

基于DDPG-PID的机器人动态目标跟踪与避障控制研究

针对机器人在动态复杂环境下的操作问题,研究机械臂跟踪运动目标及避障的运动控制方法。采用传统PID控制方法与DDPG强化学习算法相结合的方式,利用PID控制使得机械臂的工作平面快速接近目标物并与之重合,再使用DDPG算法让机械臂在平面内自主学习追踪目标物投影同时避开障碍物投影,最终在三维空间中实现跟踪与避障。该方法将机械臂作为一个智能体,智能体通过感知目标物和障碍物的距离偏差来自主学习避障跟踪策略。将本方法用于多自由度机械臂路径规划与避障任务中,对比单纯使用DDPG算法将机械臂作为智能体在空间中进行决策控制,仿真结果显示本文所提出的方法收敛效果和控制性能更好,机械臂能在训练后实现目标物的稳定跟踪和障碍物的躲避。     

元强化学习综述

元强化学习是指自动从一组相关任务中学习强化学习所需归纳偏置的相关理论和方法,对于提高强化学习算法在困难场景下的样本效率和泛化能力具有重要用途。本文提出一种新的元强化学习框架,指出设计和分析一个元强化学习算法需要同时考虑学习经验（相关任务）、归纳偏置及学习目标3个独立因素及这3个因素之间的依赖关系。在此基础上对该领域的研究现状进行了分析和总结,特别对近年来元强化学习若干文献进行了分析和归类,并详细阐述了几种代表性算法的原理及各自特点。本文还对元强化学习常用的实验环境和性能评价方法进行了介绍,对该领域的不足和未来的发展方向进行了讨论和分析。     

Mining Spatial Associations with Limited Sensory Information

Abstract

Human navigation in an unknown environment requires an understanding of the spatial relationships of the terrain. For example, a soldier who is on a reconnaissance mission in a new city needs to “know” the spatial layout of the surroundings with high confidence. Oftentimes, this understanding must be acquired within a very short amount of time and with limited sensory inputs. The soldier would benefit from a digital avatar that draws inferences about the spatial layout of the city based on an initial set of observations and guides the soldier either in further exploring the environment or in making decisions based on these inferences. In this paper, we present and evaluate an inductive approach to learning spatial associations using sensory data that is available from the simulation environment of a computer game, Unreal Tournament. We study two kinds of spatial relationships between nodes on a level of a game map: nodes that are placed near each other to satisfy some spatial requirement and nodes that are placed near each other to satisfy the design preferences of a level architect. We show that we can infer both kinds of relationships using an association rule mining algorithm. Furthermore, we show how to use an ontology to distinguish between these relationships in order to discover different types of spatial arrangements on a specific map. We discuss how the inferred associations can be used to control an avatar that makes recommendations for navigating unexplored areas on a map. We conclude with some thoughts on the applicability of our methods to scenarios in the real world, beyond the simulation environment of a game, and on how the learned associations can be represented and queried by a simple question-answer type system.     

基于产学研模式的飞机机务维修实训平台建设

目的探索构建校内生产性实训基地;方法以飞机结构腐蚀与控制实训平台的建设为例;结果阐述校内实现以产学研相结合模式来进行实训平台建设;结论对其它机务维修专业课程的实训平台建设起到引导的作用。     

Student and Teacher Attitudes towards the Role of the Mother Tongue in English Class: Report of a Questionnaire Study

This article presents the results of a questionnaire study on the role of the mother tongue in English class. The participants were 50 students and 10 teachers. The goal of the study was to investigate (1) the attitudes of both teachers and students towards the use of Chinese; (2) the actual and potential function of Chinese in English class. The results of this study indicate that Chinese was still quite extensively used in English class,that both the teachers and the students respond positively to a reaso...     

原型理论对中国大学生英语词汇学习的启示

词汇学习是我国大学生英语学习的重点以及难点,传统的机械性背记方法耗时多,收效少。就提高我国大学生英语词汇学习效率这一问题,从认知语言学的原型理论出发,为我国大学生学习词汇提供了更科学,更高效的学习方法。     

Interface Familiarity Restores Active Advantage in a Virtual Exploration and Reconstruction Task in Children

Abstract

Active exploration is reportedly better than passive observation of spatial displacements in real environments, for the acquisition of relational spatial information, especially by children. However, a previous study using a virtual environment (VE) showed that children in a passive observation condition performed better than actives when asked to reconstruct in reality the environment explored virtually. Active children were unpractised in using the input device, which may have detracted from any active advantage, since input device operation may be regarded as a concurrent task, increasing cognitive load and spatial working memory demands. To examine this possibility, 7–8-year-old children in the present study were given 5 minutes of training with the joystick input device. When compared with passive participants for spatial learning, active participants gave a better performance than passives, placing objects significantly more accurately. The importance of interface training when using VEs for assessment and training was discussed.     

FAST FEATURE RANKING AND ITS APPLICATION TO FACE RECOGNITION

A fast feature ranking algorithm for classification in the presence of high dimensionahty and small sample size is proposed. The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations, whereas neighboring points of different classes are no longer to stick to one an- other. Applying this assumption, an optimization problem weighting each feature is derived. The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time. Extensive exper- iments are conducted to validate the significance of selected features using the Yale, Extended YaleB and PIE data- sets. The thorough evaluation shows that, using one-nearest neighbor classifier, the recognition rates using 100-- 500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms, while using support vector machine features selected by the algorithm show less prominent improvement. Moreover, the experiments demonstrate that the proposed algorithm is particularly effi- cient for multi-class face recognition problem.