中国传统纺织服饰图案研究述评及价值阐释

目的 了解近年来传统纺织服饰图案的研究动态及发展趋势，归纳学术研究成果并进行评价，总结研究传统服饰图案的意义和对现代设计的启示。方法 基于史论视角、社会文化视角、工艺美术视角和设计艺术视角梳理相关文献，结合现有研究分析中国传统服饰图案的资料来源及其特色、传统服饰图案的研究热点、新时代传统服饰图案的生存策略及中国传统服饰图案创新设计的应用领域。结果指出传统服饰图案的研究史料取材丰富、研究类型呈现多元；当前传统服饰图案的传承与创新体现了数字化发展、美育引导、政策支持的特点；传统服饰图案在服装设计、公共空间、文创产品中大放异彩。结论 中国传统纺织服饰图案研究多点开花，但缺乏系统整体的“中国传统纺织服饰图案知识谱系”用于指导相关研究和实践，对中国传统纺织服饰图案的研究多处于实证分析的层面且欠缺深入独到的理论，通过综述与价值阐述，提出研究的不足之处，纵深学术研究，同时为中国传统纺织服饰图案的现代设计提供新思路。     

新时代背景下电力精准扶贫模式研究及建议

发挥电力行业优势特点,开展电力精准扶贫,对打赢脱贫攻坚战具有十分重要的作用,并能够助推脱贫攻坚与乡村振兴战略有效衔接.本文从电力扶贫在脱贫攻坚的重要作用出发,以“三聚焦”“三优先”“三模式”“三保障”为指引,详尽梳理并总结了电力精准扶贫主要模式和经验.在此基础上,深刻剖析了当前电力扶贫面临的困难及问题,提出电力精准扶贫的策略,以期为下一步开展电力精准扶贫提供参考与借鉴.     

Elastic instabilities,microstructure transformations,and pattern formations in soft materials

Large deformations of soft materials can give rise to the development of various elastic instabilities. The phenomenon is associated with a sudden and dramatic change in structure morphologies. The underlying mechanism is crucial for the formation of complex morphologies in biology. Moreover, the concept of instability-induced pattern transformations is promising for designing novel materials with switchable functions and properties. In this paper, we review the state of the art in elastic instability phenomena in soft materials. We start by considering the classical buckling in beam-based structure lattice designs. Then, we discuss the instability-induced microstructure transformations in soft porous materials, and heterogeneous multiphase and fiber composites. Next, the mechanisms – often involving the post-buckling consideration – leading to the wrinkling and folding, creasing, fringe, and fingering are discussed.     

A tool for the automatic generation and analysis of regular analog layout modules

This paper describes the characteristics of a new CAD tool that enables the creation of layout libraries of selected analog modules. This Analog Modules Generator (AMG) automatically creates multiple layout versions of two commonly used analog structures: the differential pair and arrays of series-connected or stacked devices, for the subsequent generation of layout libraries. Based on the number of devices and rows defined by the user for the layout implementation, the tool validates all possible implementations, which are later saved in a database. Additionally, an extraction process can be optionally executed over all the layout views saved in the database. The AMG generates several reports with all the characteristics of the implemented layouts, including area and parasitic components, facilitating further statistical processing. We describe the features and capabilities of the proposed AMG tool, and several test cases are presented. Results show that suitable layout implementations can be achieved by layout and circuit designers in a very reduced amount of time.     

加权信息熵与增强局部二值模式结合的人脸识别

针对人脸识别因光照、姿态、表情、遮挡及噪声等多种因素的影响而导致的识别率不高的问题，提出一种加权信息熵（IEw）与自适应阈值环形局部二值模式（ATRLBP）算子相结合的人脸识别方法（IE （w） ATR-LBP）。首先，从原始人脸图像分块提取信息熵，得到每个子块的IEw；然后，利用ATRLBP算子分别对每个人脸子块提取特征从而得到概率直方图；最后，将各个块的IEw与概率直方图相乘，再串联成为原始人脸图像最后的特征直方图，并利用支持向量机（SVM）对人脸进行识别。在AR人脸库的表情、光照、遮挡A和遮挡B四个数据集上，IE （w） ATR-LBP方法分别取得了98.37%、94.17%、98.20%和99.34%的识别率。在ORL人脸库上，IE （w） ATR-LBP方法的最大识别率为99.85%；而且在ORL人脸库5次不同训练样本的实验中，与无噪声时相比，加入高斯和椒盐噪声后的平均识别率分别下降了14.04和2.95个百分点。实验结果表明，IE （w） ATR-LBP方法能够有效提高人脸在受光照、姿态、遮挡等影响时的识别率，尤其是存在表情变化及脉冲类噪声干扰时的识别率。     

起·承·转·合 ——解读巍宝山长春洞空间布局的结构章法

长春洞是巍宝山保存最完整的宫 观，虽占地不到两亩①，却在有限的空间中 创造出九楼十院的空间格局。本文以长春洞 空间布局序列为研究对象，从知觉体验的 连续性入手，提出了空间变化对参观者情感 的影响，论述了在建筑空间布局序列中参观 者知觉体验逐层递进的控制机制，探讨了以 “起、承、转、合”为结构，以“知觉体验”为 章法的动态空间布局设计手法，希望为道教 建筑空间与其他超越日常的精神空间的研 究提供启示。     

杭州文化创意产业区的空间发展格局及空间影响

在我国城市化发展转型的大环境下,随着城市新区的开发以及城市中心区传统产业的外迁和置换,文化创意产业区建设正日益受到城市决策者和研究者的关注。本文以杭州为例,在分析目前城市文化创意产业区建设现状的基础上,探讨了其在城市宏观层面的空间发展格局与发展的基本模式。     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Up-to-date information on urban air pollution is of great importance for environmental protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 50 million measurements. We collected the measurements throughout more than two years using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100 m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with sub-weekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error–a standard metric to evaluate the accuracy of air quality models–of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.