Nash Bargaining Solution based rendezvous guidance of unmanned aerial vehicles

This paper addresses a finite-time rendezvous problem for a group of unmanned aerial vehicles (UAVs), in the absence of a leader or a reference trajectory. When the UAVs do not cooperate, they are assumed to use Nash equilibrium strategies (NES). However, when the UAVs can communicate among themselves, they can implement cooperative game theoretic strategies for mutual benefit. In a convex linear quadratic differential game (LQDG), a Pareto-optimal solution (POS) is obtained when the UAVs jointly minimize a team cost functional, which is constructed through a convex combination of individual cost functionals. This paper proposes an algorithm to determine the convex combination of weights corresponding to the Pareto-optimal Nash Bargaining Solution (NBS), which offers each UAV a lower cost than that incurred from the NES. Conditions on the cost functions that make the proposed algorithm converge to the NBS are presented. A UAV, programmed to choose its strategies at a given time based upon cost-to-go estimates for the rest of the game duration, may switch to NES finding it to be more beneficial than continuing with a cooperative strategy it previously agreed upon with the other UAVs. For such scenarios, a renegotiation method, that makes use of the proposed algorithm to obtain the NBS corresponding to the state of the game at an intermediate time, is proposed. This renegotiation method helps to establish cooperation between UAVs and prevents non-cooperative behaviour. In this context, the conditions of time consistency of a cooperative solution have been derived in connection to LQDG. The efficacy of the guidance law derived from the proposed algorithm is illustrated through simulations.     

ARL: An adaptive reinforcement learning framework for complex question answering over knowledge base

Recently, reinforcement learning (RL)-based methods have achieved remarkable progress in both effectiveness and interpretability for complex question answering over knowledge base (KBQA). However, existing RL-based methods share a common limitation: the agent is usually misled by aimless exploration, as well as sparse and delayed rewards, leading to a large number of spurious relation paths. To address this issue, a new adaptive reinforcement learning (ARL) framework is proposed to learn a better and interpretable model for complex KBQA. First, instead of using a random walk agent, an adaptive path generator is developed with three atomic operations to sequentially generate the relation paths until the agent reaches the target entity. Second, a semantic policy network is presented with both character-level and sentence-level information to better guide the agent. Finally, a new reward function is introduced by considering both the relation paths and the target entity to alleviate sparse and delayed rewards. The empirical results on five benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model SRN, the proposed model achieves performance improvements of 23.7% on MetaQA-3 using the metric Hits@1.     

NMPC-based UAV-USV cooperative tracking and landing

The study aims to solve the problem of real time tracking and precise landing of unmanned aerial vehicle (UAV) during unmanned surface vehicle (USV) navigation. In this paper, a UAV-USV cooperative tracking and landing control strategy based on nonlinear model predictive control (NMPC) is proposed. Firstly, the UAV-USV heterogeneous intelligent body collaborative system is constructed based on the mathematical model of UAV and USV; secondly, the tracking controller is designed based on NMPC algorithm to ensure that the UAV can track the USV in real time; finally, a UAV-USV cooperative landing control strategy is proposed to realize the heave motion of the USV to the peak vertex, thus, the UAV completes the precise landing with the minimum impact. As the simulation experimental results show, the UAV-USV cooperative tracking and landing control scheme proposed in this paper can provide effective solution against real time tracking and accurate landing of UAV during the navigation of USV.     

Multiple UAV formations delivery task planning based on a distributed adaptive algorithm

In this paper, six-rotor UAVs are used in the field of distribution to realize the delivery of materials arriving the demand point by UAVs. Due to the small load capacity of the six-rotor UAV, in response to actual demand, the UAV group will be used to complete the delivery task. Considered to be close to the real requirements, trajectory constraints and dynamic obstacles are established in the trajectory planning based on group perception range. In order to better deal with dynamic obstacles and related constraints, this paper designs a distributed adaptive algorithm based on individual decision-making and group decision-making. Individual decision-making is embodied in the intelligence and adjustment of UAVs, involving actor-critic methods, artificial potential field method ideas and probability finite state machines; group decision-making is embodied in the leadership mechanism and joint decision-making; self-adaptation is embodied in the adaptive adjustment of UAV level in group. In order to avoid collisions between UAV groups, a conflict resolution algorithm is designed. Through simulation analysis, the distributed adaptive algorithm proposed in this paper can not only satisfy all constraints, avoid dynamic obstacles stably, and complete tasks with small fluctuations, but also obtain the most successful decision-making UAV in the group. This article further analyzes and discusses the relevant parameters in the algorithm, and obtains the optimal parameter ranges in individual decision-making and group decision-making.     

无人机应用发展关键基础设施与低空公共航路网规划

当代无人机特点是数量众多、应用范围广、活跃频次高和用户众多。对于安全运行，高标准的无人机软硬件系统可靠性和鲁棒性至关重要。无人机运行高度依赖于新技术和外部高技术基础设施。其中，低空公共航路网（又称低空“天路”）是一种保障低空无人机安全和高效运行的关键新型基础设施。深入研究与推进低空“天路”为核心的无人机应用基础设施建设对于发展城市空中交通网、助力智慧交通发展非常有意义。文章在梳理无人机应用关键基础设施的基础上，进行了基于低空“天路”的新型基础设施建设的未来展望。     

Multi-UAVs collaborative tracking of moving target with maximized visibility in urban environment

Unmanned aerial vehicles (UAVs) with limited field of view are utilized to track a moving ground target continuously in urban environment. In urban environment, the sight lines of UAVs to the target are easily blocked by dense obstacles. To overcome this difficulty, the model predictive control (MPC) based collaborative tracking control is proposed with the goal of the maximum visibility of target. First, a visible probability based performance index is proposed, and the flight planning strategy of maximum the phase difference is obtained as a consequence. Then a centralized MPC based collaborative control problem is solved to obtain the optimal control signals. The joint cost function consists of four parts which aims at tracking target, avoiding collision, avoiding the blind area and maintaining the maximum visibility, respectively. The effectiveness of the proposed collaborative strategy is verified by simulation. Compared with the traditional MPC-based collaborative method, the proposed maximum visible probability index provides an optimal dynamic formation structure for multi UAVs to guarantee the tracking of the moving ground target in urban environment.     

高中生合作学习中合作技能的现状及培养对策

合作学习已被广泛运用于高中英语课堂,但其优势未完全被发掘,在实施过程尚有值得探讨的问题,值得注意的是学生合作技能的欠缺对合作学习效果的影响。本文通过问卷和访谈对高中生合作技能的现状进行调查及分析,并对提高高中生合作技能提出相应的培养建议。     

Iterative rule-guided reasoning over sparse knowledge graphs with deep reinforcement learning

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to empower retrieval systems, recommender systems, and question answering systems, generating a surge in research interest. Recently developed reasoning methods usually suffer from poor performance when applied to incomplete or sparse KGs, due to the lack of evidential paths that can reach target entities. To solve this problem, we propose a hybrid multi-hop reasoning model with reinforcement learning (RL) called SparKGR, which implements dynamic path completion and iterative rule guidance strategies to increase reasoning performance over sparse KGs. Firstly, the model dynamically completes the missing paths using rule guidance to augment the action space for the RL agent; this strategy effectively reduces the sparsity of KGs, thus increasing path search efficiency. Secondly, an iterative optimization of rule induction and fact inference is designed to incorporate global information from KGs to guide the RL agent exploration; this optimization iteratively improves overall training performance. We further evaluated the SparKGR model through different tasks on five real world datasets extracted from Freebase, Wikidata and NELL. The experimental results indicate that SparKGR outperforms state-of-the-art baseline models without losing interpretability.     

基于差异化理念的高中物理合作学习策略探究

无论是高中物理新课标还是新高考改革,都强调要重视学生差异,进行个性化教学,以满足学生的不同需要。合作学习是现代教育提倡的教学方式之一,将差异化理念融入高中物理合作学习之中,既满足了学生的不同需要,又培养了学生与他人合作的能力。因此,本文对这一教学方式的教学策略进行了较深入的探究,提出了相应的指导策略、组织策略和实施策略。     

合作学习、组织柔性与创新方式选择的关系研究

本文针对企业在快速变化环境下出现的创新不确定性增加的问题和目前相关理论研究的不足,在基于资源和基于能力理论的基础上,利用组织学习和技术创新等相关理论,分析了合作学习对组织的资源柔性、能力柔性的影响作用;并对资源柔性和能力柔性如何影响创新方式的选择,如何降低创新风险以提高持续竞争优势进行了深入探讨。     

新课程理念下合作学习的价值探究——基于陕师大教育学院“三下乡”对旬邑县新课程实施状况的调研

将＂合作学习＂作为一种全新的学习方式引入课堂教学的实施过程,是基础教育课程改革的一项重要任务。如何能够有效地组织学生开展合作学习,是目前普遍关注的热点问题。但通过笔者调研发现,合作学习在课堂实施过程中,仍存在许多误区,如师生缺乏对合作学习价值的正确认识,学校缺乏适合学生进行合作学习的教学环境,及在合作学习中师生关系与角色转换等问题,这些导致合作学习不能真正发挥出促进学生全面发展的优势。为此,在新课程理念的引领下,本文对这几个问题作出一些探讨。     

初中数学“学案导学”教学中“导”的策略初探

教师在＂学案导学＂教学中的＂导＂大有学问。它既包括新课之＂导＂、问题情景之＂导＂、学习过程之＂导＂,更包括学习方法之＂导＂、思维方式之＂导＂、师生互动和学生互助之＂导＂等等。总之,＂导＂贯穿于教和学的全过程,应该说＂学案导学＂教学中的＂导＂是一门值得探究的学问和艺术。     

数字化学习港集约化建设体系研究初探

本文初步探讨了如何有效地为数字化学习港的正常运行提供各项保障措施,从而最大限度地实现数字化学习港的资源和运营的集约化目标,揭示了在全国建设终身学习体系和学习型社会的背景下,以多种远程服务技术为依托的数字化学习港的建设模式和运行机制,为数字化学习港的发展找到了新的关键措施。     

Composite cooperative synchronization and decentralized learning of multi-robot manipulators with heterogeneous nonlinear uncertain dynamics

This paper studies the problem of composite synchronization and learning of multiple coordinated robot manipulators subject to heterogeneous nonlinear uncertain dynamics under the leader-follower framework. A new two-layer distributed adaptive learning control scheme is proposed, which consists of the first-layer distributed cooperative estimator and the second-layer decentralized deterministic learning controller. The first layer aims to enable each robotic agent to estimate the leader’s information. The second layer is responsible for not only controlling each individual robotic agent to track over desired reference trajectory, but also accurately identifying/learning each robot’s nonlinear uncertain dynamics. Design and implementation of this two-layer distributed controller can be carried out in a fully-distributed manner, which do not require any global information including global connectivity of the communication network. The Lyapunov method is applied to rigorously analyze stability and parameter convergence of the resulting closed-loop system. Numerical simulations on a team of two-degree-of-freedom robot manipulators have been conducted to demonstrate the effectiveness of the proposed results.     

数学课上如何提高小组合作学习的有效性

分组学习模式是解决当前教育问题的有效学习模式,它有利于提高课堂教学效率,培养学生的自学能力、合作与交流意识、责任感以及自主意识。合理分组,科学的课堂组织教学,合理的课堂评价机制是小组合作学习有效性的关键。     

A novel architecture to identify locations for Real Estate Investment

The identification of a favorable location for investment is a key aspect influencing the real estate market of a smart city. The number of factors that influence the identification easily runs into a few hundreds (including floor space area, crime in the locality and so on). Existing literature predominantly focuses on the analysis of price trends in a given location. This paper aims to develop a set of tools to compute an optimal location for investment, a problem which has received little attention in the literature (analysis of house price trends has received more attention). In previous work the authors proposed a machine learning approach for computing optimal locations. There are two main issues with the previous work. All real estate factors were assumed to be independent and identically distributed random variables. To address this, in the current paper we propose a network structure to derive the relational inferences between the factors. However, solving the location identification problem using only a network incurs computational burden. Hence, the machine learning layers from the previous work is combined with a network layer for computing an optimal location with proven lower computational cost. A second issue is that the computations are performed on an online database which has inherent privacy risks. The online data, user information and the algorithms can be tampered through privacy breaches. We present a privacy preservation technique to protect the algorithms, and use blockchains to secure the identity of the user. This paper presents solutions to two interesting problems in the analysis of real estate networks: a) to design tools that can identify an optimal location for investment and b) to preserve the privacy of the entire process using privacy preserving techniques and block chains.     

改变教学方式打造高效课堂

小组合作学习是新课标倡导的一种重要学习方式和教学组织形式。为了进一步优化地理课堂教学,更好地体现“自主学习、探究学习、合作学习”的教学新理念,我一直在不断转变自己的教育理念,积极尝试一些新的想法和做法。通过自己的学习和实践,我深深体会到,要打造优质、高效的地理课堂,关键在于有效地组织好小组合作学习。     

移动学习用户隐私信息披露行为实证研究

[目的/意义]通过构建移动学习用户隐私信息披露行为影响因素的理论模型,探究移动学习用户隐私行为的影响关系,以提高用户隐私信息披露意愿和对隐私信息的控制能力。[方法/过程]本文通过对隐私信息披露文献的查阅,运用问卷调查和实证研究相结合的方法,从行为态度、主观规范和行为控制3个方面分析了移动学习用户隐私信息披露行为,利用结构方程对提出的假设进行分析验证。[结果/结论]数据研究结果表明,隐私信息披露意愿对隐私信息披露行为有正向影响,感知移动学习收益性、移动学习用户社会影响、隐私控制自我效能和感知移动学习便利性对隐私信息披露意愿呈正向影响,而感知移动学习风险性则对隐私信息披露意愿呈负向影响。本研究能够帮助移动学习平台开发商更好地收集信息,为用户定制个性化的服务。[局限]研究的调查对象覆盖面较窄,对年龄、性别等因素之间的关系缺乏深入地分析。     

企业间合作经验的学习效应研究述评

按照通常经验曲线理论,企业间合作经验的积累将会提升企业的合作绩效,但是实证研究却得出相互矛盾的结论．通过文献梳理,将企业间合作经验学习效应归纳为三个模型：基于合作能力的、边际递减的和不确定性调节下的学习效应模型．进一步提出下一步研究的方向．