高校新旧校区景观文脉的传承

经过轰轰烈烈的新校区建设热潮之后,高校校园的建设面临着文化景观的建设问题.如何使旧校区的景观文脉在新校区中得以传承和发展,是新校园景观建设的重要问题.对新校区而言,继承旧校区的特色比创造新的文化景观更有实际意义.     

基于局部特征匹配的目标跟踪研究

针对目标跟踪中的特征提取和匹配问题进行分析,提出了一种基于局部特征匹配的目标跟踪方法,该算法基于Shape Context进行特征提取。首先,对现有特征提取算法进行简单介绍,并详细介绍Shape Context的基本思想,基于此提出基于局部特征的跟踪算法。其次,详细阐述了目标跟踪算法：采用目标点的χ2-test直方图距...     

户外广告设计中符号语境的构建

通过商品符号结构、符号与环境的契合度二方面来分析现代户外广告设计中符号语境的构建过程,并结合案例加以说明。阐述了符号语境的构建可以将广告中的信息塑造成更加完整的传播形态。从而使信息更好地得到传播,也能使受众更加容易接受、理解与选择,进而能够更好地实现有效信息的传递以及提高信息传播的价值,进一步了解符号语境的运用在户外广告中的重要作用。     

基于辅助源和相关熵的ADS-B信息时延估计新算法

提出了一种基于辅助源和相关熵的ADS-B信息时延估计新算法。首先,对报文信息时延产生机制进行了研究,提出新的时延模型;其次,以辅助源信号所包含的多普勒频移特征为研究对象,在信号相似性比较时引入相关熵提出时延估计算法;然后,分别研究了扫描步长与高斯核长对本时延估计精度的影响;最后,通过仿真实验验证了在不同噪声条件下本算法的时延估计性能。与已有方法相比,本算法不需要更改报文格式,在脉冲噪声条件下具有更高的时延估计精度,故具有更好的实际应用价值。     

建筑符号学理论在室内设计中的应用研究——以广东石油化工学院实验楼为例

符号学经过多年的发展,已被广泛应用于各个领域。建筑符号学作为符号学系统的一类,对建筑、室内设计实践有着极其重要的指导意义。文章以广东石油化工学院实验楼室内设计实践为例,从理论基础、主题文脉、设计理念、符号衍生、符号运用等方面,阐释了建筑符号学在室内设计中的提取、演绎、创作与识读历程。其在设计实践领域的运用,能将设计聚"形"、延"意"、传"神",能有效地使空间设计继承文化、延伸意义和内涵,让空间充满生命力。如此,既丰富了设计的理论、构思与表达,又为信息的传递提供了媒介,也为解决建筑、室内设计中"文化缺失"、"意义危机"等问题提供了重要途径。     

居住区规划设计中的工业遗产保护与再利用模式研究——以西安市陕西重型机械厂改造规划设计为例

随着对工业遗产价值的认知,探索工业遗产保护与再利用途径,已渐成为国内外城市延续历史文脉、彰显特色的重要举措。近年来,城市工业厂区搬迁后被转化为居住用地的比例呈压倒性趋势,如何将居住区开发和工业遗产保护紧密结合,是城市可持续发展面临的重要而紧迫的课题。该文通过西安市陕西重型机械厂改造规划的设计研究,提出工业遗址区的居住区规划设计思路和框架。从厂区资源、规划定位、社区需求以及经济性诸方面确定厂区的更新策略,探寻工业遗产保护与居住区道路系统、绿化系统、公建系统、空间环境系统规划设计的契合点,构建具有鲜明工业文化特色的现代居住社区。     

基于历史文脉视角的泰安高铁站广场设计解析

泰安高铁站广场是反映泰安市文化内涵和时代发展的公共开放空间。该文以尊重当地历史文脉为高铁站广场设计原则,通过推导高铁站广场与泰山主峰的视线关系模型,精确控制高铁站广场周边地块的建筑尺度,建立高铁站广场与泰山主峰之间良好的视通廊道;通过发掘地域历史文化元素"东岳真形图",在高铁站广场空间构架设计中引入该图案的形态结构,形成独具泰安地域特征的公共开放空间体系。     

Telling tales: using stories to remake energy policy

To benefit and protect the populace, government policies often promise aspirational changes to current practice. Different kinds of narratives are important in the framing, explanation, motivation, and understanding of policies and strategies. For example, the UK government's 2008 Climate Change Act proclaimed that all new homes will be zero carbon by 2016. This ‘hero story’, where society is ‘saved’ by clever technologies, is inspiring, positive and familiar. An alternative is the ‘learning story’, where things are not quite as simple as they first seemed. In a learning story, protagonists are normal people who need to overcome a challenge. In energy policy, the learning story could address the gap between the technical potential and what is achieved in practice. Three real-world examples from retrofit and new-build projects are used to show how implicit narratives can create conflict when the tellers (e.g. researchers) have to tell one kind of story but have data for the other. Recommendations are provided for a balanced approach to the deployment of different kinds of tales by policy-makers, researchers, implementers and users. Harnessing the learning story and developing a ‘caring story’ could motivate policy-makers and the public to invest effort in building performance.     

Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy (V_O) dynamics in an oxide‐based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second‐order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems.