空间基因

在形态类型相关研究基础上,从城市空间发展理论的视角,提出"空间基因"概念。空间基因是独特的、相对稳定的空间组合模式,它既是城市空间与自然环境、历史文化长期互动契合与演化的产物,承载着不同地域特有的信息,形成城市特色的标识,又起着维护三者和谐关系的作用。通过空间基因识别提取、解析评价到传承导控的技术体系,可以避免采用统一的模式面对千差万别城市、不尊重历史和自然的设计弊端,强化规划设计的在地性,推动城市规划设计方法从空间形式创作到空间基因分析的方向性转变,为城市建设与自然保护、文化传承的共赢提供有效设计路径。文章还对空间基因库计划进行了展望。     

Locality‐Conscious Nested‐Loops Parallelization

To speed up data‐intensive programs, two complementary techniques, namely nested loops parallelization and data locality optimization, should be considered. Effective parallelization techniques distribute the computation and necessary data across different processors, whereas data locality places data on the same processor. Therefore, locality and parallelization may demand different loop transformations. As such, an integrated approach that combines these two can generate much better results than each individual approach. This paper proposes a unified approach that integrates these two techniques to obtain an appropriate loop transformation. Applying this transformation results in coarse grain parallelism through exploiting the largest possible groups of outer permutable loops in addition to data locality through dependence satisfaction at inner loops. These groups can be further tiled to improve data locality through exploiting data reuse in multiple dimensions.     

A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing

How to reduce power consumption of data centers has received worldwide attention. By combining the energy-aware data placement policy and locality-aware multi-job scheduling scheme, we propose a new multi-objective bi-level programming model based on MapReduce to improve the energy efficiency of servers. First, the variation of energy consumption with the performance of servers is taken into account; second, data locality can be adjusted dynamically according to current network state; last but not least, considering that task-scheduling strategies depend directly on data placement policies, we formulate the problem as an integer bi-level programming model. In order to solve the model efficiently, specific-design encoding and decoding methods are introduced. Based on these, a new effective multi-objective genetic algorithm based on MOEA/D is proposed. As there are usually tens of thousands of tasks to be scheduled in the cloud, this is a large-scale optimization problem and a local search operator is designed to accelerate convergent speed of the proposed algorithm. Finally, numerical experiments indicate the effectiveness of the proposed model and algorithm.     

东非铬钒钙铝榴石（察沃石）宝石的矿物学和地球化学研究现状

东非（包括肯尼亚、坦桑尼亚和马达加斯加）铬钒钙铝榴石（察沃石）均赋存于新元古代莫桑比克变质带的钙质硅酸盐石墨片麻岩中。矿物学研究表明,东非察沃石为宝石级钒（±铬）钙铝榴石,以ω（V2O3）≈0.20%或ω（Cr2O3）≈0.30%为分界线可划分为钒钙铝榴石、铬钙铝榴石和铬钒钙铝榴石3类,其中以钒钙铝榴石为主。东非察沃石的晶体化学式为（Ca2.792～3.005Fe0～0.013 Mn0.008～0.080 Mg0.022～0.078）2.948～3.110（Al1.787～1.968Ti0.005～0.028Cr0.001～0.072Fe0～0.080V0.005～0.163）1.890～2.052[Al0～0.288Si2.712～3.017O12],可简写成Ca3（Al,Cr,Fe,V）2[SiO4]3;其端员组分为Grs87.61～96.79Gld0.16～5.66Uvr0.02～2.46Pyr0.73～2.68Sps0.28~2.75Adr0.00～2.71Alm0.00～0.43,表明它由钙铝榴石（87.61%～96.79%）、钙钒榴石（0.16%～5.66%）和钙铬榴石（0.02%～2.46%）组成。以往研究表明,东非不同国家所产的察沃石之间V/Cr质量分数比值存在明显差异,它们的化学成分是可变的,但同一矿床的V/Cr质量分数比值通常是不变的;并且它们还具有3组一致的δ＾18 O值,依次为9.5‰~11.0‰、11.6‰～14.8‰和15.5‰～21.1‰,利用O同位素与V-Cr-Mn微量元素含量相结合的方法,可鉴别其地质和地理来源。     

论风情商业街地点性的实现

风情商业街的地点性是在地域性的框架下,依据场地的多种特质,构建独一无二的场所及氛围。文章以重庆渝中区的洪崖洞民俗风貌区和融创·白象街两个项目为例,从文态、形态、业态、生态四个方面进行比较分析,说明风情商业街地点性的成因及其重要意义,并提供一种规划设计思路。     

基于局部保持投影和核直接判别分析的掌纹识别

为了提高识别性能,提出运用局部保持投影(LPP)和核直接判别分析(KDDA)相结合的方法进行掌纹识别.在小样本图像识别中,为了解决特征方程矩阵的奇异性,首先运用图像下抽样降低掌纹空间的维数,然后应用LPP提取掌纹局部结构特征作为KDDA的输入提取分类特征,计算特征向量间的余弦距离进行掌纹匹配.运用PolyU掌纹图像库,...     

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection(OLPP),a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition(EMD),and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine(MWSVM) and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.     

用于分类的样本保局鉴别分析方法

针对高维数据分类中鉴别特征降维方法的小样本问题和有效维度丢失问题,结合最新提出的片对齐框架和保局投影提出了样本保局鉴别分析方法.该方法通过分别构造每个样本的类内近邻图和类外近邻图,并将所有样本的类内近邻图和类外近邻图结合起来,形成了所有样本的类内近邻和类外近邻关系.然后,在使所有样本的类内近邻尽可能地聚集在一起的同时使...     

新中式建筑风格的地域性解读

每个时代都有每个时代各自的特征,但沉淀在建筑深层次内的文化内涵是不变的。对新事物的接受与对旧有经典事物的继承,使人们尝试着把传统融合在现代建筑设计中,把对古老文化的理解体现在设计的流行趋势中。现代人对传统建筑的延续,并不是简单地去做大屋顶、木结构,并不是让这些古老的元素、古老的价值,简单的复活。而是尝试着从生活、从需要出发,用发展的眼光,在原有地域文化的基础上去发展它。     

Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes

Soft sensor techniques have been widely used to estimate product quality or other key indices which cannot be measured online by hardware sensors. Unfortunately, their estimation performance would deteriorate under certain circumstances, e.g., the change of the process characteristics, especially for global learning approaches. Meanwhile, local learning methods always only utilize input information to select relevant instances, which may lead to a waste of output information and inaccurate sample selection. To overcome these disadvantages, a new local modeling algorithm, adaptive local kernel-based learning scheme (ALKL) is proposed. First, a new similarity measurement using both input and output information is proposed and utilized in a supervised locality preserving projection technique to select relevant samples. Second, an adaptive weighted least squares support vector regression (AW-LSSVR) is employed to establish a local model and predict output indices for each query data. In AW-LSSVR, instead of using traditional cross-validation methods, the trade-off parameters are adjusted iteratively and the local model is updated recursively, which reduces the computational complexity a lot. The proposed ALKL is applied to an online crude oil endpoint prediction in an industrial fluidized catalytic cracking unit (FCCU) process. The experimental results demonstrate the high precision of our ALKL approach.