Ecological Planting Imperative: Functional Systems,Not Stylized Ecologies

The ability of landscape architectural projects to mitigate the worst effects of climate change will depend upon designed ecological systems. These systems will be built with plants. Despite the recognition of ecology as an essential driver of landscapes, the professionals of landscape architecture too often lack the knowledge and practical skills to create robust vegetative systems. New approaches and tools are required. This article outlines principles and methods for designing biodiverse plant systems for urban sites. Planting methods that increase species richness, functional diversity, and spatial complexity are emphasized as a way of developing more resilient plantings. Selecting species with similar evolutionary adaptions to stress, disturbance, and competition—as well as creating multi-layered compositions of diverse plant morphologies—allows designers to create compatible, long-lived plant mixes. To balance the increased visual complexity of diverse plant mixes, the article explores design techniques to make plantings more appealing to the public. The strategies explored here are based on the projects, experience, and research of Phyto Studio, a Washington, D.C. based studio. The methods build on work described in the author’s book, Planting in a Post-Wild World, an exploration of how to create designed plant communities.     

Antenna selection for MIMO system based on pattern recognition

This paper proposes a novel Multiple-Input Multiple-Output (MIMO) transmission scheme based on Pattern Recognition (PR), which is termed as the PR aided Transmission Antenna Selection MIMO (PR-TAS aided MIMO). As the conventional TAS algorithms need to search all possible legitimate antenna subsets, they may impose some redundant calculations. In order to avoid this problem, we employ some pattern recognition methods to carry out the TAS algorithm in this paper. To be specific, two PR algorithms, namely the K-Nearest Neighbor (KNN) algorithm and the Support Vector Machine (SVM) algorithm, are introduced and redesigned to obtain a TAS with lower complexity but higher efficiency. Moreover, in order to improve the performance of the SVM, we propose a new feature extraction of channel matrix for the TAS. Our simulation results show that the proposed KNN and SVM based PR-TAS algorithms are capable of striking a flexible tradeoff between the complexity and the Bit Error Rate (BER), and the new feature can effectively improve the BER performance compared with the conventional feature extraction method.     

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。     

生态健康旅游袜产品的创意设计与开发

介绍生态健康旅游袜产品的创意设计思路及工艺设计,包括产品定位设计、功能设计、品质设计、外观设计、包装设计。阐述生态健康旅游袜产品的生产工艺流程,包括纤维生产工艺、短流程快速纺纱生产工艺、生态健康旅游袜生产工艺。详细分析生态健康旅游袜产品主体纤维材料的选择,并以生态健康旅途袜产品开发为例,介绍袜品研发过程,并对袜品进行性能测试。总结生态健康旅游袜的产品定位原则、创意设计思想及研发关键技术,对功能性袜品的设计开发具有借鉴意义。     

基于宜兴彩陶山水图案的女式针织衫设计

对宜兴彩陶装饰艺术中典型的山水图案进行分析,阐述宜兴彩陶山水图案的文化内涵。分析宜兴彩陶山水图案的审美特征,包括浓淡有序的色彩层次、抽象凝练的造型肌理、形饰一体的结构布局。以各类针织纱线为原料,采用岛精电脑横机,对女式针织衫进行款式、图案及组织结构设计。详细介绍基于宜兴彩陶山水图案元素的女式针织衫色彩、款式、图案和肌理设计应用实例。该研究为女式针织衫的设计提供了新的思路,为中国传统装饰艺术元素在服饰品中的跨界设计提供了理论与实践参考。     

A disruption recovery plan in a three-stage production-inventory system

This paper proposes a recovery plan for managing disruptions in a three-stage production-inventory system under a mixed production environment. First, a mathematical model is developed to deal with a disruption at any stage while maximizing total profit during the recovery-time window. The model is solved after the occurrence of a disruption event, with changed data used to generate a revised plan. We also propose a new and efficient heuristic for solving the developed mathematical model. Second, multiple disruptions are considered, where a new disruption may or may not affect the recovery plans of earlier disruptions. The heuristic, developed for a single disruption, is extended to deal with a series of disruptions so that it can be implemented for disruption recovery on a real-time basis. We compare the heuristic solutions with those obtained by a standard search algorithm for a set of randomly generated disruption test problems, and that show the consistent performance of our developed heuristic with lower computational times. Finally, some numerical examples and a real-world case study are presented to demonstrate the benefits and usefulness of our proposed approach.     

Teaching sustainable design in architecture education: Critical review of Easy Approach for Sustainable and Environmental Design (EASED)

In the current context of climate change and ecological awareness, designing sustainable environments is definitely understood as a shared responsibility. With the construction sector consuming half of the world׳s energy, the role of some key stakeholders such as architects becomes even more critical when providing responsible and relevant design for the built environment. Thus, improving the way our environments are being designed challenges some cultural systems that show evident limits, such as the training of future architects and engineers.In this research, the focus is on architecture students and aims to demonstrate how the use of a new sustainable performance simulation tool, called Easy Approach for Sustainable and Environmental Design (EASED) could contribute to educate them about innovatively sustainable design. This was assessed through the evaluation of student engagement, their use of the tool and its appropriation. Results show that individual work was not convincing, whereas success was met during group work. Limits and improvement possibilities were found in the interface of EASED as well as in the educational set up of the tool.     

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.     

Evolving accuracy: A genetic algorithm to improve election night forecasts

In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provides an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.