基于具身认知的博物馆自然交互设计研究

具身认知强调了身体-环境-认知之间的相互联系，主张在特殊环境中身体的感知状态与物理属性对认知的形成有根本影响，为传统展示形式造成用户感知受限的博物馆环境中的自然交互设计研究提供理论参考。文章从具身认知视角出发探索博物馆中的自然交互设计方法，首先采用VOSViewer软件分析具身认知设计近几年相关研究热点与发展趋势，结合用户旅程图法分析现阶段博物馆自然交互展示的痛点与触点，利用具身认知理论提出情景重构、行为沉浸和知觉延伸的具身交互设计策略，最终通过新加坡天福宫具身交互设计作品验证博物馆自然交互设计方法。     

基于Android终端的物联网物流管理系统的设计与实现

随着现代物流服务业的快速发展,如何在配送过程中为客户提供尽可能详细的信息和咨询服务,成为物流企业的发展重点。“物联网”是继计算机、互联网之后的世界信息产业的第三次产业革命。物联网中的RFID,GPS,嵌入式软件等技术在物流管理系统有着广泛的应用。但这些技术往往需要特有的终端,价格昂贵,采集信息单一,且缺乏处理客户查询请求与送货监控功能的接口。随着智能手机尤其是Android手机的普及,移动应用越来越广泛,为智能移动终端在物流管理中的应用提供了可能。本文介绍了Android终端定位技术,条码扫描技术,并对相关技术方案进行了深入研究和扩展。最后在此基础上实现了基于Android终端的物联网物流管理系统。     

数字城市中快速自动生成三维景观的研究

数字地球、数字中国、数字城市是当前数字信息化领域的一个研究热点。其中的一个关键问题是利用遥感图像形成三维景观,其处理方法是首先将遥感图像矢量化,然后以矢量化数据为基础完成三维立体化工作以形成三维景观。本文阐述了以矢量化数据为基础完成三维立体化工作以形成三维景观的技术方法,包括获取矢量化数据、快速自动生成三维景观等处理步骤。应用效果良好。     

云计算环境下基于智能终端的探究性学习实践

信息技术的发展,以及情境认知与学习理论、分布式认知理论等学习认知理论的发展,对当前教育教学方式提出了新的要求,笔者尝试在云计算环境中基于智能终端开展探究性学习,探索信息技术在高中生社会实践课程中的应用形式。     

自动识别技术：物联网时代的切入点

展会同期,“创新大数据应用,促进产业发展研讨会”“2014物联网技术在零售与物流业创新应用研讨会”也顺利召开。大数据与物联网、数据采集、二维码等自动识别技术的互赢互利、大数据的未来发展趋势、大数据带来的产业发展机会。围绕商品二维码标识、移动物联、云标识平台服务等内容,探讨零售物流领域物联网技术的创新应用等话题成为两个论坛的热点。以下是部分与会专家、企业代表的观点汇集,以飨读者。     

认知无线电研究进展

随着无线通信广泛的应用,移动互联网业务的需求高速增长,静 态的频谱规划政策使得可用的频谱资源越来越匮乏。认知无线电（Cognitive radio, CR）在感知频谱环境的基 础上,有目的地实时改变工作参数,实现对频谱的高效利用,为解决当前频谱资源利用率低 的问题提供了理论与技术支持。目前,CR技术已得到了广泛的关注。本文首先对认知无线电 体系及标准的研究现状作了简要介绍,然后深入地分析了频谱感知、频谱共享及功率控制3 项关键技术,最后对认知无线电的应用前景进行总结,包括电视白频谱、能量收集及5G网络 3个方面,并对未来异构泛在网络环境下开展基于认知无线电技术的无线资源管理、无缝切 换及负载均衡等方面的研究进行了展望。     

基于射频识别技术在嵌入式仓储物流管理中的研究

为了提高仓储物流的信息化水平、降低物流成本、为上下游企业提供良好的物流服务,该文研究在嵌入式仓储物流管理中的射频识别技术。首先研究射频识别技术,包括系统组成、工作原理和关键技术;然后研究实际应用,包括物品监控、物流控制、便携式数据采集、移动载体定位、扩展应用等。结果表明,射频识别技术是智能化仓储物流管理的基础,是未来的发展方向。     

基于人机交互的认知数字流形

对脑内"宇宙"观测、探索脑认知规律是本世纪科学的重要目标。人脑信息加工机制既不能从外部直接检测,也不能用解剖方法对心理过程进行微观直接观测,只能用计算机模拟,或称"黑箱"方法借助理论思维对脑内活动和机理做间接推断是50多年认知科学的隐含假设,称为"人机分离的功能模拟"研究范式。从哲学互补原理可洞察到"人机融合的结构观测"范式,即直接观测、采集脑认知内容数据,获得脑认知结构及运动规律。此方法可能吗?大胆想法是把脑内认知诱导到脑外环境中观测,但认知会发生在脑外吗?基于认知哲学前沿的脑外认知观,我们提出基于人机交互的"认知耦合"脑信息观测思路。基于人机交互构建"脑认知体-耦合情境-信息流形"(BSM)认知观测数字系统。基于范畴、态射、素描等理论,把人机情境中的交互过程投影为图、网络等可视化数理模型上,高维认知流形降维到复杂认知网络,实现认知结构成像、认知耦合调制及认知逻辑、认知动力系统的研究。     

栅格图像自动矢量化系统的研究与实现

研究了自动矢量化地图所需的理论,并基于.NET平台进行了算法实现。以实现自动矢量化为目标,按照传统获取矢量化数据的流程进行探索。从数字图像处理和数学形态学的相应理论角度出发,分析了自动矢量化系统的功能;讨论了扫描电子地图二值化的实现、腐蚀膨胀和开闭运算理论在电子地图去噪中的应用、基于模式识别技术的图元细化与识别等难点技术。针对上述技术,在.NET平台上,基于GDI+技术,通过实例验证了自动矢量化系统的可行性及有效性。     

情境法在《软件工程》教学中的应用

软件工程是一门实践性极强的工程学科,在知识的学习过程中要注重其在实践中的运用.根据该课程的特点,提出了在教学中应用情境认知理论,运用各种情境教学模式帮助学生灵活掌握知识并培养其真正解决问题的能力.     

MERLEAU-PONTY,EMBODIED COGNITION,AND THE PROBLEM OF INTENTIONALITY

Because embodied cognition is a new approach in cognitive science and artificial intelligence, it is in need of a well-formulated theoretical foundation. We believe that Merleau-Ponty's work will prove useful in this endeavor because of the similarities between his work and embodied cognition. We will briefly sketch out some of these similarities and will show how Merleau-Ponty's work can be coupled with embodied cognition in order solve the problem of intentionality.     

Quantum Physical Symbol Systems

Because intelligent agents employ physically embodied cognitive systems to reason about the world, their cognitive abilities are constrained by the laws of physics. Scientists have used digital computers to develop and validate theories of physically embodied cognition. Computational theories of intelligence have advanced our understanding of the nature of intelligence and have yielded practically useful systems exhibiting some degree of intelligence. However, the view of cognition as algorithms running on digital computers rests on implicit assumptions about the physical world that are incorrect. Recently, the view is emerging of computing systems as goal-directed agents, evolving during problem solving toward improved world models and better task performance. A full realization of this vision requires a new logic for computing that incorporates learning from experience as an intrinsic part of the logic, and that permits full exploitation of the quantum nature of the physical world. This paper proposes a theory of physically embodied cognitive agents founded upon first-order logic, Bayesian decision theory, and quantum physics. An abstract architecture for a physically embodied cognitive agent is presented. The cognitive aspect is represented as a Bayesian decision theoretic agent; the physical aspect is represented as a quantum process; and these aspects are related through von Neumann’s principle of psycho-physical parallelism. Alternative metaphysical positions regarding the meaning of quantum probabilities and the role of efficacious choices by agents are discussed in relation to the abstract agent architecture. The concepts are illustrated with an extended example from the domain of science fiction.     

Enactive artificial intelligence: Investigating the systemic organization of life and mind

The embodied and situated approach to artificial intelligence (AI) has matured and become a viable alternative to traditional computationalist approaches with respect to the practical goal of building artificial agents, which can behave in a robust and flexible manner under changing real-world conditions. Nevertheless, some concerns have recently been raised with regard to the sufficiency of current embodied AI for advancing our scientific understanding of intentional agency. While from an engineering or computer science perspective this limitation might not be relevant, it is of course highly relevant for AI researchers striving to build accurate models of natural cognition. We argue that the biological foundations of enactive cognitive science can provide the conceptual tools that are needed to diagnose more clearly the shortcomings of current embodied AI. In particular, taking an enactive perspective points to the need for AI to take seriously the organismic roots of autonomous agency and sense-making. We identify two necessary systemic requirements, namely constitutive autonomy and adaptivity, which lead us to introduce two design principles of enactive AI. It is argued that the development of such enactive AI poses a significant challenge to current methodologies. However, it also provides a promising way of eventually overcoming the current limitations of embodied AI, especially in terms of providing fuller models of natural embodied cognition. Finally, some practical implications and examples of the two design principles of enactive AI are also discussed.     

基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

Language as a Cognitive Tool

The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is ‘linguistic’ in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic ‘language’ we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself.     

Computational intelligent brain computer interaction and its applications on driving cognition

Driving is one of the most common attention-demanding tasks in daily life. Driver's fatigue, drowsiness, inattention, and distraction are reported a major causal factor in many traffic accidents. Due to the drivers lost their attention, they had markedly reduced the perception, recognition and vehicle control abilities. In recent years, many computational intelligent technologies were developed for preventing traffic accidents caused by driver's inattention. Driver's drowsiness and distraction related studies had become a major interest research topic in automotive safety engineering. Many researches had investigated the driving cognition in cognitive neuro-engineering, but how to utilize the main findings of driving-related cognitive researches in traditional cognitive neuroscience and integrate with computational intelligence technologies for augmenting driving performance will become a big challenge in the interdisciplinary research area. For this reason, we attempt to integrate the driving cognition for real life application in this study. The implications of the driving cognition in cognitive neuroscience and computational intelligence for daily applications are also demonstrated through two common attention-related driving studies: (1) cognitive-state monitoring of the driver performing the realistic long-term driving tasks in a simulated realistic-driving environment; and (2) to extract the brain dynamic changes of driver's distraction effect during dual-task driving. Experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.     

Reconfigurable swarm robots produce self-assembling and self-repairing organisms

Reconfigurable robots are set to become a vital factor in the theoretical development and practical utilization of robotics. The core problem in this scientific area is steady information transfer between a swarm and its organisms and vice versa. To this end, we present a basic theoretical framework that stipulates the interoperation between the two modes. We evaluate our proposed framework by constructing 100 mobile microrobots of three different types that initiate the processes of self-reconfigurability and self-repair. The autonomous decision to self-aggregate to an organism mainly derives from the necessity to overcome existing obstructive environmental conditions, e.g. ramps or clefts. The methodological dichotomy that we have chosen to evaluate our concept was to pursue in parallel an approach based on embodied distributed cognition and an evolutionary path mainly based on artificial genomes and reproduction. In this paper, we evaluate these two different approaches in two distinct grand challenges and present the main results.     

The Linguistic Subversion of Mental Representation

Embedded and embodied approaches to cognition urge that (1) complicated internal representations may be avoided by letting features of the environment drive behavior, and (2) environmental structures can play an enabling role in cognition, allowing prior cognitive processes to solve novel tasks. Such approaches are thus in a natural position to oppose the ??thesis of linguistic structuring??: The claim that the ability to use language results in a wholesale recapitulation of linguistic structure in onboard mental representation. Prominent examples of researchers adopting this critical stance include Andy Clark, Michael Wheeler, and Mark Rowlands. But is such opposition warranted? Since each of these authors advocate accounts of mental representation that are broadly connectionist, I survey research on formal language computation in artificial neural networks, and argue that results indicate a strong form of the linguistic structuring thesis is true: Internal representational systems recapitulate significant linguistic structure, even on a connectionist account of mental representation. I conclude by sketching how my conclusion can nonetheless be viewed as consistent with and complimentary to an embedded/embodied account of the role of linguistic structure in cognition.     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research

The development of robotic cognition and the advancement of understanding of human cognition form two of the current greatest challenges in robotics and neuroscience, respectively. The RobotCub project aims to develop an embodied robotic child (iCub) with the physical (height 90 cm and mass less than 23 kg) and ultimately cognitive abilities of a 2.5-year-old human child. The iCub will be a freely available open system which can be used by scientists in all cognate disciplines from developmental psychology to epigenetic robotics to enhance understanding of cognitive systems through the study of cognitive development. The iCub will be open both in software, but more importantly in all aspects of the hardware and mechanical design. In this paper the design of the mechanisms and structures forming the basic 'body' of the iCub are described. The papers considers kinematic structures dynamic design criteria, actuator specification and selection, and detailed mechanical and electronic design. The paper concludes with tests of the performance of sample joints, and comparison of these results with the design requirements and simulation projects.