A COLLABORATIVE SPECTRUM SENSING SCHEME USING FUZZY COMPREHENSIVE EVALUATION IN COGNITIVE RADIO

Cognitive Radio(CR) is a promising technology to solve the challenging spectrum scarcity problem.However, to implement CR, spectrum sensing is the groundwork and the precondition.In this paper, a collaborative spectrum sensing scheme using fuzzy comprehensive evaluation is proposed.The final sensing decision of the proposed scheme is based on the combination of distributed sensing results of different Secondary Users(SUs).To improve the reliability of the sensing decision, the combination procedure takes into account the credibility of each SU, which is evaluated using fuzzy comprehensive evaluation.The effect of the presence of malicious SUs and malfunctioning SUs on the performance of the proposed scheme is also investigated.The efficiency of the scheme is validated through analysis and simulation.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach

In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted (with some assumptions) in terms of optimal incoherent diversity addition for “virtual diversity branches” or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT) for single user detection. The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the NPT noise immunity characteristics: probability of misdetection error (PM) and probability of false alarm (Pfa) or Receiver Operational Characteristics (ROC) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (ROC) of the optimum approach is provided as well. Finally, the diversity combining approach is applied for the collaborative spectrum sensing and censoring. It is shown how the diversity addition principles are applied for distributed detection algorithms, called hereafter as SIMO radar or distributed SIMO radar, implementing Majority Addition (MA) approach and Weighted Majority Addition (WMA) principle.

Multi-Agent System for Real Time Planning Using Collaborative Agents

Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment;where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.

A Cognitive Model for Multi-Agent Collaboration

In multi-agent system, agents work together for solving complex tasks and reaching common goals. In this paper, we propose a cognitive model for multi-agent collaboration. Based on the cognitive model, an agent architecture will also be presented. This agent has BDI, awareness and policy driven mechanism concurrently. These approaches are integrated in one agent that will make multi-agent collaboration more practical in the real world.

A Notation to Support Exploring Collaboration in Design Teams

Collaboration in large projects is a major challenge for contemporary architectural practice and this paper presents a notation for describing and analyzing how these collaborations can take place.Based on an extensive literature review some opportunities were found for the development of a notation that combined three particular aspects:network diagrams,Euler diagrams and a model from architectural design.An exploratory case study was conducted based on the collaboration during a complex architectural project,which combined three approaches:document analysis,semi-structured interviews with key stakeholders and a comparison of the documents with results from the interviews.The notation demonstrated to be suitable for two purposes:to improve the understanding of particular design events and as support material for presenting and exploring collaborations.Results indicate that the notation was suitable,comprehensible and flexible,and it demonstrated good value when used in speculative ways,such as an intermediary design artifact that supports discussion and improvements in the collaboration process,which indicates promising future directions.It can also be argued that,similar to a sketch,the notation can also support the process of planning and“designing”the interaction between teams in design fields and even in other project-based organizations.

协作场景下基于生理数据的认知投入测量研究

认知投入是决定学习有效发生的实质性投入,在协作学习中尤为关键。但在协作学习中学习者认知投入不均衡容易导致“搭便车”等负面学习行为的发生。并且,由于认知投入具有内隐性,对其的精准测量仍面临挑战。而基于客观生理数据,有望打开协作场景下认知投入测量“黑箱”。为此,研究从唤醒度和适应度两个维度描述协作场景下的认知投入,以184名参与“海绵校园设计”协作任务的大学生为研究对象,利用便携式生理设备采集学习者的皮肤电数据与心率数据,构建协作场景下认知投入的测量模型。研究结果表明:学习者的交感神经活动可以有效反映唤醒度水平,副交感神经活动则可以更加精准地测量适应度水平。通过信号处理技术与特征工程方法,研究从生理数据的时域、频域、形态三个层面构建15维特征,运用主成分回归方法验证得出,特征集合可以显著解释认知投入水平变异的39.7%,显示出测量模型良好的信效度,其中适应度体现了认知投入在协作任务中的定向,因而相比唤醒度具有更高的教育价值;在协作过程中,学习者的唤醒度呈现先降后升的趋势,适应度则持续提升。研究丰富了认知投入的理论,并为教师理解学生学情提供依据,有助于协作学习成效的改进与提升。

认知群体感知工具对在线协作学习知识建构水平的影响研究

认知群体感知工具是协作学习者感知群体认知信息、提升学习效果的重要技术支撑。不同信息复杂程度的认知群体感知工具对在线协作学习中学习者的知识建构水平影响程度如何尚需进一步探索。文章从认知信息复杂度出发,设计了实时的显性/隐性认知群体感知工具,并通过对比实验,利用统计分析、卡方检验、认知网络分析,分析两种认知群体感知工具对在线协作学习知识建构水平的影响程度。研究结果表明:两种认知群体感知工具有助于在线协作学习中高水平知识建构行为的发生。显性/隐性认知群体感知工具支持下的学习者知识建构行为特征及知识建构模式存在差异性。其中,显性认知群体感知工具能促进协商型对话和升华型对话;隐性认知群体感知工具则更能促进协商型对话的生成。研究结论有助于教师调整教学策略,引导学习者完成协作任务并提升知识构建水平。

协作问题解决中平等参与对大学生认知投入的影响机制研究

协作问题解决能力被视为智能时代学习者的关键素养之一,要求每位共同体成员投入必要的认知努力。尽管该领域的相关研究已广泛开展,但对于协作问题解决中认知投入的过程机理,特别是成员平等参与如何影响认知投入的作用机制尚未充分阐释。以某高校178位大学生作为研究对象,探究协作问题解决中不同平等参与程度的小组在认知投入方面的差异。通过分析小组绩效和参与均衡度,将小组划分为四种类型,并采用内容分析及过程挖掘技术比较各类小组在认知投入水平、过程模式和典型序列上的差异。结果显示,高绩效小组倾向于观点整合,而高均衡小组则更多地展现出同伴支持。小组绩效与参与均衡度的交互作用表明,在参与均衡的小组中,与任务相关的知识水平是促进认知投入的关键因素;而在参与不均衡的小组中,主导者的角色则对认知投入有显著影响,其中“引导者”角色相较于“独行者”更能促进良好的认知投入。研究结果丰富了智能时代下协作问题解决中认知投入过程机制的理解,也为针对不同类型小组的有效学习干预提供了理论依据。

项目式多粒度协作学习模型促进工程教育中高阶认知参与的实证研究

认知参与是协作学习中小组表现的一个重要指标。本研究提出了一种项目式多粒度协作学习模型,将项目式学习与多粒度协作学习策略结合起来,以提高工程教育中设计类课程的认知参与和学习绩效。为了验证模型的有效性,从一门工程设计类课程中采集了学习者进行数据库系统设计时的协作会话数据。数据分析显示,在基于项目式多粒度协作学习模型的小组合作过程中,学习者的高阶认知参与被不断提升。其中,反思活动可以极大地提高学习者的高阶认知参与。此外,个体准备和同伴评估中的高阶认知参与可以极大地提高协作中的小组表现。这些发现对协作学习活动的组织具有参考价值。项目式多粒度协作学习策略可以提升工程设计类课程中学习者的高阶认知参与和小组绩效。教师应重点关注个体准备和同伴评估活动,激发学习者在这两类活动中的高阶认知参与,这可以帮助学习者在小组协作中取得更大的进步。

“行为-认知”二元视角下协同创作知识建构特征研究——基于过程挖掘和认知网络的方法

协同创作作为协作学习的重要形式,对培养学习者高阶思维和深层次认知能力具有重要意义。协同创作产生的大量伴随式数据能映射学习者知识建构过程,进而发掘出学习者阶段性知识建构特征。基于此,从“行为-认知”二元视角,运用过程挖掘和认知网络分析方法,对协同创作过程中不同阶段、不同水平学习者的研讨话语数据进行深入挖掘,分析学习者内隐的行为与认知,揭示其知识建构特征与规律。结果表明:(1)从行为视角看,社交和共享型行为是知识建构的核心活动;高分组知识建构发展遵循“共享-冲突-协商-共识”规律,低分组在对话中的探索、协商行为难以推进知识建构交互层次;当出现认知冲突时,高分组易同化同伴观点,而低分组易顺应同伴观点。(2)从认知视角看,随着知识建构的深入,高阶思维和深层理解逐渐凸显,认知结构随时间多元化发展,但“分析-关联结构”始终保持为基础路径,且活动中期是高、低分组学习者认知发展分水岭。分析学习者的知识建构特征有助于教育工作者更好地诊断学习者协作过程中存在的潜在问题,从而调整教学策略以促进其知识构建过程。

“以弱协强”何以成功?——基于“程序-认知”框架的案例研究

新世纪是“新公共管理运动”迈向“合作治理”的时代,然而合作不是自动发生的。在强制型合作背景下,弱势能部门采取哪些策略建立内部合法性,推动强势能部门“真诚合作”,现有文献尚未给予足够重视并深入探究具体的实现策略。因此,整合合作治理研究中相对割裂的政治学和组织管理的两种路径,将程序和认知这两种独立的路径整合为理解内部合法性的两个不同层面,从而构建起弱势能部门建立内部合法性的“程序—认知”分析框架,进而以X市学校体育场地设施对外开放政策的跨部门合作作为典型个案,探讨弱势能部门推动强势能部门合作过程中获取内部合法性的策略。案例研究结果表明,纵向权威、社会需求可以为弱势能部门带来较强的外部合法性,而内部合法性的建设则需经历对话参与、协商谈判和承诺协议等三重程序阶段,同时伴随共享认识、理解信任和内生认同三重认知层面的层层生发才得以实现。在“以弱协强”这一约束性情境中奏效的策略,将在约束条件放松或其他支持性条件下获得更为广阔的施展空间。

在线协作会话中教师干预对学习者认知加工过程的影响研究

在线协作会话是一种广泛应用于在线学习和混合式学习的协作学习活动,教师干预则被认为是提升在线协作会话效果的重要手段之一。然而,教师干预到底如何影响学习者的认知加工过程尚不明确。文章以某大学31名教育技术学专业学生为研究对象,通过准实验研究探索在线协作会话活动中教师干预对学习者认知加工过程的影响。研究结果显示:教师干预对学习者的行为参与没有显著影响;对学习者观点的认知复杂性有抑制作用,而对观点的认知科学性有促进作用;能够促使学习者拥有更丰富的认知模式,使其观点从“前科学”更多地向“具体的事实和解释”转换。基于上述结论,文章进一步得出如下启示:在线协作会话中,教师可以通过设置最低参与标准来促进学习者的行为参与;学习者可以从在线协作会话的教师干预中获益,形成更丰富的认知模式;教师的干预应更加注重对学习者观点的认知复杂性的引导。

军用无人系统协同感知与分布式滤波技术

现代战争由机械化向无人化转型,而人工智能、大数据等技术的发展促使未来战争形态呈现出仿生智能的特点。仿生无人机、智能无人车等载荷打击平台通过卫星数据链开展典型区域联合作战的前提是已知敌我双方的准确位置信息。围绕军用无人运动平台的演化历程和发展趋势,对无人系统的构成和特点进行归纳,进而指出多传感器协同感知是信息获取的主要形式,通过分布式滤波处理得到平台的位置和姿态。对多平台协同感知能力、协作估计策略和SLAM技术进行分析和总结,指出融合深度智能的协同认知、数据驱动和多源多模态融合等技术是提升无人作战效能的核心关键。

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Hybrid-augmented intelligence： collaboration and cognition

The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

基于多尺度目标检测的人机协作装配场景认知方法

人机协作装配场景的快速理解对提高协作机器人认知能力、实现人机协作装配具有重要的现实意义。针对非结构化人机协作装配场景认知过程中目标尺度差异较大、缺少统一场景描述框架等问题,首先构建了一种轻量级多尺度目标检测网络LMS-Net,并在网络训练过程中引入目标检测框聚类机制以提高多尺度目标检测精度;然后将LMS-Net检测结果转换为人物交互图并建立了人机协作装配场景元描述模型,提出了基于多尺度目标检测的人机协作装配场景认知方法。在自建数据集HRC-Action上的实验结果表明所构建多尺度目标检测网络具有较高的准确率(平均89%)和较快的速度(深度学习工作站平均58.7 FPS,Jetson Nano B01嵌入式开发板平均25 FPS),所提人机协作装配场景认知方法具有较好的可行性和实用性。

Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps

Collaborating with a squad of Unmanned Aerial Vehicles(UAVs)is challenging for a human operator in a cooperative surveillance task.In this paper,we propose a cognitive model that can dynamically adjust the Levels of Autonomy(LOA)of the human-UAVs team according to the changes in task complexity and human cognitive states.Specifically,we use the Situated Fuzzy Cognitive Map(Si FCM)to model the relations among tasks,situations,human states and LOA.A recurrent structure has been used to learn the strategy of adjusting the LOA,while the collaboration task is separated into a perception routine and a control routine.Experiment results have shown that the workload of the human operator is well balanced with the task efficiency.

基于模糊认知图的老旧小区改造协同治理机理研究

老旧小区改造实践中政府、企业、居民等多方主体协同不足以成为掣肘改造推进的关键问题。已有研究以识别与定性分析部分协同影响因素为主,从系统视角分析协同影响要素间相互作用机理的定量化研究较少;尤其缺乏定量化测度与分析多个协同因素结构与功能的分析工具。本研究首先基于协同治理理论,识别并搭建了以“驱动—过程—结果”三类协同要素所构成的分析框架;创新引入模糊认知图法构建老旧小区改造协同治理FCM模型;并通过对若干模型中心性指标的静态与动态分析模拟,从多视角分析老旧小区改造情境下影响多主体协同达成的关键因素及其作用机理。分析结果解释了改造主体间协同不足原因,同时验证了FCM方法在相关研究中的适用性。

价值嵌入与价值对齐:人类控制论的幻觉

价值对齐反映了对人类智能与人工智能关系进行简化的理论惰性,其本质潜藏着一种价值维度上的人类中心主义投射,同时也体现了人类在技术自治性面前的不安感。价值对齐是一种企图将所有的技术-社会的复杂性问题都置于“价值偏差”的系统性盈余中予以整体性解决的技术治理方法。然而,将人类价值观嵌入人工智能系统,一方面会使得技术的发展成为最小化人类责任的借口;另一方面,这种话语以一种抽象性的人机价值差异掩盖了人工智能技术发展全过程中的价值性因素,从而以价值排他性的方式将人工智能的“问题”建构为一种绝对意义上的外在,使得关于价值的讨论只逡巡在人机的边界之上,而不向人类内部返回。从这个意义上来说,人工智能已经建构起来的社会历史中的价值无意识和不平衡的数据分布现实,是一个关于“具有自主性的智能体应该和不应该被如何设计”的问题。因此,人工智能的价值判断基础应当由外在的价值嵌入转向具身认知和知觉能力的形成。只有通过有效的人机交互与环境感知,打造开放式人机生态体系,鼓励开发与人类处于更广泛的目的分享和责任分担的人工智能,才能防范由技术所导致的任何形式的垄断。