“Laboratorio Q”, Seville: creative production of collective spaces before and after austerity

The creative city approach is going through a redefinition after the 2008 global financial crisis. In the specific case of South-European cities, in the context of austerity and cuts in public investment, creativity is becoming a strategy for achieving maximum social benefit and improvement of the built environment with minimum economic expenditure. This paper looks at this redefinition of creativity through the case study of Seville, in southern Spain. Through research methods that include video-recorded testimonies of the actors involved, mapping at the online platform “Laboratorio Q”, and public engagement activities, this paper explores how the civic society, professional, and public authorities have reinvented how to produce collective spaces. The paper concludes that bottom-up creative processes for producing collective spaces have become more visible since the 2008 crisis, when architects, planners, public authorities and policy-makers have been “learning” from them.     

融合环境因素的有限空间毒气扩散危害程度分析

为探究环境因素对毒气扩散危害程度的影响,通过对比有限空间内外环境差异,了解气体在有限空间内受力情况,并以传统高斯烟羽模型为基础,考虑扩散气体与有限空间边界碰撞产生的反射作用,建立了适用于有限空间气体扩散的改进高斯烟羽模型。深度剖析有限空间气体扩散环境,挖掘气体扩散主要关联因子,构建风速、地表粗糙度对有限空间气体扩散的影响函数;参照有限空间环境特征,分别设置3种不同风速与2种不同地表粗糙度,推求6种不同环境条件下气体扩散面积,阐明风速与地表粗糙度对有限空间气体扩散的影响程度。结果表明:在单一风速条件下,风速越高,地表粗糙度越低,洞室内H2S气体滞留时间越短,洞室内气体的扩散面积越小;风速越低,地表粗糙度越高,洞室内气体的扩散面积越大。     

风速时间序列混沌判定方法比较研究

混沌识别是对非线性时间序列进行混沌预测的前提。针对时间序列风速确定性与随机性相结合的复杂非线性特征,研究了不同的混沌识别方法,并对风速时间序列进行混沌特征识别。应用随机噪声、周期运动及经典混沌系统的时间序列对所选方法进行可靠性验证。对美国国家风能研究中心M2测风塔实测时间序列风速数据进行非线性混沌特征识别。结果表明:风速时间序列具有明显的混沌特征;各风速时间序列表现出不同程度的混沌特征;各混沌识别方法对风速时间序列混沌特征的表达形式不同,互为补充,相互验证。     

基于修正等速运动规律的自由曲面多轴加工刀轴序列优化方法

针对自由曲面多轴加工中起始位置处刀具相对工件速度、加速度不为零导致的一定程度刚性冲击现象,提出了一种基于修正等速运动规律的刀轴序列优化方法,通过控制工件坐标系中决定刀具姿态的两个约束角,使之变化遵循修正等速运动规律,获得了切削行上平稳变化的刀轴序列,避免了由于起始位置处刀具相对工件速度、加速度不为零导致刀具不能精准到达后续加工位置的现象。算例分析表明,该方法能产生平稳且光滑的刀轴序列,可一定程度改善机床运动性能,提高加工表面质量。     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real‐time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision‐based systems to avoid hazards as well as plan safe routes. However, vision‐based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision‐based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this study, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared with traditional fixed‐sensor configurations.     

Robust SVM-direct torque control of induction motor based on sliding mode controller and sliding mode observer

This paper proposes a design of control and estimation strategy for induction motor based on the variable structure approach. It describes a coupling of sliding mode direct torque control (DTC) with sliding mode flux and speed observer. This algorithm uses direct torque control basics and the sliding mode approach. A robust electromagnetic torque and flux controllers are designed to overcome the conventional SVM-DTC drawbacks and to ensure fast response and full reference tracking with desired dynamic behavior and low ripple level. The sliding mode controller is used to generate reference voltages in stationary frame and give them to the controlled motor after modulation by a space vector modulation (SVM) inverter. The second aim of this paper is to design a sliding mode speed/flux observer which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The effectiveness of the whole composed control algorithm is investigated in different robustness tests with simulation using Matlab/Simulink and verified by real time experimental implementation based on dS pace 1104 board.     

传统村落码头空间改造的适宜性评价研究

综合运用Yaahp软件分析、层次分析法、Delphi法、文献解析与问卷调研等方法,对功能改造活动的调研数据建立判断矩阵,选取22个指标并计算推演指标权重,建立传统村落码头的功能改造的适宜性评价模型,并提出相应的量化评价方法,推导出传统村落码头空间功能改造适应性的七个关键要素。为传统村落的文化传承与功能提升提供决策依据。     

SFEP文本因果关系提取及其与SFN转化研究

为将系统故障演化过程(system fault evolution process，SFEP)的文本描述转化为空间故障网络(space fault network，SFN)结构，用于故障分析，本文提出SFEP文本因果关系提取方法，及其与SFN基本结构的转化方法。首先给出SFEP中事件的几种典型因果关系。随后提出因果关系与SFN基本结构的转化流程。本文方法围绕着关键字和因果关系组模式展开，通过模型的不断学习补充和丰富关键字和组模式。最终使方法具备将SFEP文本转化为SFN结构的能力。以飞机起落架故障发生过程文本为例进行了应用，实验结果表明该方法可用于SFEP文本中的因果关系分析，并得到了理想的SFN。完善的关键字和组模式有利于使用计算机智能处理SFEP的SFN。     

基于多元神经网络融合的分布式资源空间文本分类研究

针对实体产业对科技资源的服务需求,以服务效应作为资源文本分类标准,提出一种基于多元神经网络融合的分布式资源空间文本分类模型。设计了包含词嵌入层、卷积层、双向门控循环单元层、注意力机制层和softmax层的多元神经网络通路;在此基础上采用基于需求—效应—资源分类策略,完成了从定性科技资源需求到定量资源服务效应求解,再到定性科技资源输出的映射变换,重点解决了分布式科技资源局部和全局语义特征形式多样、文本长距离依赖特征显著、重要资源信息难以准确识别的问题,进而从分布式科技资源空间中快速准确地获取效应知识,提升实体产业产品研发效率和创新能力;通过万方专利科技资源数据集验证了所提方法的可行性和有效性,为更加全面地挖掘资源文本特征和按需服务实体产业提供了一种新的思路和手段。