城市高温灾害的规划应对研究进展

随着全球气候不断升温,城市高温 灾害越来越严重,严重影响城市人居环境。 通过CNKI和Web of Science检索城市高温 灾害相关文献,采用Met a-a na lysis方法和 CiteSpace工具,分析近年来城市规划应对高 温灾害的研究进展。结果发现,城市高温灾 害的规划应对研究起源于城市热岛方面,在 气候变化方面得到发展,最后形成城市高温 灾害的研究。在减缓性策略与适应性策略两 大主题下,主要应对途径是土地利用、空间 结构、道路交通、绿化景观以及建筑等,每种 途径又分为多种具体策略。目前在规划体系、 针对性规划以及效益评估等方面存在明显不 足。在理论上应加强构建规划体系,以及效 益评估系统的研究;在实践中制定应对高温 灾害的规划编制标准,探索气候友好型城市 规划设计方法,为韧性城市建设提供参考。     

Online decision making and automatic decision model adaptation

The paper investigates an online version of the vehicle routing problem with time windows, in which additionally arriving requests cause the revision of so far followed routes and schedules. An extended online optimization framework is proposed, which automatically adapts to problem variations and enables the explicit consideration of up-to-date knowledge about the current performance of the controlled system. Actually, we use the mean punctuality observed in the transportation system to adjust the objective function utilized for solving the next decision problem instance. The search is guided toward least cost solutions coming along with high punctuality. We prove the applicability of this approach within comprehensive numerical experiments.     

On control of adaptation in parallel mesh generation

A black-box methodology of a parallel generation of adaptive anisotropic meshes is described. A control of mesh adaptation, important for robustness and flexibility, may effect parallel properties of the methodology. This is demonstrated with a 3D example.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of the Snakebot.     

Agent-based simulation of electricity markets: a survey of tools

Agent-based simulation has been a popular technique in modeling and analyzing electricity markets in recent years. The main objective of this paper is to study existing agent-based simulation packages for electricity markets. We first provide an overview of electricity markets and briefly introduce the agent-based simulation technique. We then investigate several general-purpose agent-based simulation tools. Next, we review four popular agent-based simulation packages developed for electricity markets and several agent-based simulation models reported in the literature. We compare all the reviewed packages and models and identify their common features and design issues. Based on the study, we describe an agent-based simulation framework for electricity markets to facilitate the development of future models for electricity markets.     

一种基于用户的自适应类选择算法

针对QoS管理模型中的自适应处理模块进行了深入研究,提出了基于用户的自适应类选择算法,该算法不仅能够提高用户的满意度,而且能够均衡各个服务类别在网络中的负荷,保证应用的服务质量,实现QoS管理的自适应性.算法采用了基于下一时刻的预测方法进行估算,从而优化了参数取值,使用户选择的服务类别更加精确.     

两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.     

顺应理论视阈下《红楼梦》称谓语的翻译

根据 Verschueren 的语言顺应论,动态顺应是翻译策略,直译、意译、归化、异化都是动态顺应过程中主动顺应或被动顺应时表现出的一些具体方法。汉语称谓语的翻译是译者动态顺应译语语言现实和社会规约从而实现翻译目的的一个过程。因此,分析比较《红楼梦》杨译本与霍译本两个英文全译本中对称谓语的翻译,指出翻译方法的选择取决于是否顺应译语文化并能实现译者的翻译动机。     

Review of adaptation mechanisms for data-driven soft sensors

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In order to be able to provide a comprehensive overview of the adaptation techniques, adaptive soft sensing methods are reviewed from the perspective of machine learning theory for adaptive learning systems. In particular, the concept drift theory is exploited to classify the algorithms into three different types, which are: (i) moving windows techniques; (ii) recursive adaptation techniques; and (iii) ensemble-based methods. The most significant algorithms are described in some detail and critically reviewed in this work. We also provide a comprehensive list of publications where adaptive soft sensors were proposed and applied to practical problems. Furthermore in order to enable the comparison of different methods to standard soft sensor applications, a list of publicly available data sets for the development of data-driven soft sensors is presented.