基于空间句法的传统村落保护规划编制应用的思考——以国家级历史文化名村唐模为例

传统村落保护规划的编制是解决传统村落保护发展基础性和全局性的工作。运用空间句法的相关理论与技术对唐模古村进行空间解析,利用量化后的空间分析结果对传统村落保护规划中的保护区划划定、街巷路网调整、功能结构布局等重要规划内容提供了依据。廓清了空间句法在传统村落保护规划中应用的若干问题,为传统村落保护规划编制提供了可借鉴的技术路线和修正的思考。     

皖西山区美丽乡村建设的现状与对策研究——以金寨县双河村为例

皖西山区因地理环境、经济产业及建设发展历程不同于省域其他地区,故美丽乡村建设有其自身的特点。本文基于对其多个乡村的整体布局、建筑风貌、空间环境、基础设施等开展的田野调查,以该地区的金寨县双河镇双河村为例,阐述了皖西山区美丽乡村建设的共性;并结合所参与的实际工程项目,着眼于技术层面,从产业、规划和建筑的角度,相应地提出了引导支撑、按需统筹、分类更新的对策,为该地区的美丽乡村建设提供一定的理论依据和实例参考。     

驾驶员转向特性分类与在线辨识研究

针对汽车驾驶员转向特性分类与在线辨识问题,基于驾驶模拟器实验台,设计典型实验工况,选取具有一定驾驶经验的驾驶员进行实验研究。采集驾驶员转向过程实验数据并提取特征值,应用模糊C均值聚类算法将驾驶员转向特性分为谨慎型、一般型和激进型三类;根据分类实验数据应用BP神经网络算法建立驾驶员转向特性辨识模型,对模型辨识准确度进行离线验证,并基于辨识模型设计在线辨识方法。实验结果表明:研究方法能够实现对驾驶员转向特性的合理分类和在线准确辨识。     

党员分类教育管理的实践与思考

党员分类教育管理,就是正视党员的个体差异,积极探索党员共性要求与个性需要的有机融合,调动和发挥党员的先锋模范作用,进一步增强基层党组织的凝聚力、战斗力。中国石化股份胜利油田分公司清河采油厂南区工作站在这方面进行了有益的尝试,取得了很好的效果,具有一定的推广价值。     

Automatic classification of defects on the product surface in grinding and polishing

Grinding and polishing are standard operations in material processing. It is important to inspect and classify the potential defects existing on the product surfaces after grinding and polishing in order to obtain high quality in both functionality and aesthetics. Post-processing handling can be carried out after the defects have been correctly classified. A vision system already exists to detect and classify defects based on captured grayscale images automatically. The system is able to find and locate the defects precisely, but is incapable of placing those defects into the right predefined classes. The old system classifies the defects 30% of the time into the 15 predefined classes based on shape features. In this paper, a new classification strategy has been introduced using diverse extracted features. In addition to shape features, some other feature extraction methods were tried, e.g. Laws filter bank, DCT (Discrete Cosine Transformation) filter bank, Gabor filter bank, and statistical features based on co-occurrence matrix. The Support Vector Machine (SVM) was used as a multi-class classifier with input of the extracted features. By combining the Gabor filter features and the statistical features, the classification rate of the system can reach 82% overall right rate, which is applicable practically.     

Morphological analysis and classification of types of surface corrosion damage by digital image processing

This paper examines a new concept of corrosion surface damage analysis by using the digital image processing. Corrosion phenomena are analyzed using a digital value for morphological surface damages instead of electrochemical methods. Initial images are characterized by three categories: color, texture and shape features. To calculate corrosion surface damages color we use the interpretation of HIS model. For the texture attributes, the method of co-occurrence matrix is used. Five types of corrosion damage are examined. Multidimensional scaling procedure is used to define the classification plane. This study suggests a probabilistic method of decision-making. This analysis develops a method for automated identification system of corrosion damages and is supposed to be more advantageous than that of electrochemical techniques.     

Trajectory-based human action segmentation

This paper proposes a sliding window approach, whose length and time shift are dynamically adaptable in order to improve model confidence, speed and segmentation accuracy in human action sequences. Activity recognition is the process of inferring an action class from a set of observations acquired by sensors. We address the temporal segmentation problem of body part trajectories in Cartesian Space in which features are generated using Discrete Fast Fourier Transform (DFFT) and Power Spectrum (PS). We pose this as an entropy minimization problem. Using entropy from the classifier output as a feedback parameter, we continuously adjust the two key parameters in a sliding window approach, to maximize the model confidence at every step. The proposed classifier is a Dynamic Bayesian Network (DBN) model where classes are estimated using Bayesian inference. We compare our approach with our previously developed fixed window method. Experiments show that our method accurately recognizes and segments activities, with improved model confidence and faster convergence times, exhibiting anticipatory capabilities. Our work demonstrates that entropy feedback mitigates variability problems, and our method is applicable in research areas where action segmentation and classification is used. A working demo source code is provided online for academical dissemination purposes, by requesting the authors.     

一种基于蚁群优化的图像分类算法

现有图像降维方法中特征信息被过多压缩,从而影响图像分类效果。提出IC-ACO算法,利用蚁群算法来解决图像分类问题。算法充分提取并保留图像的各种形态特征。利用蚁群优化算法在特征集中自动挖掘有效特征和特征值,构建各类分类规则,从而实现图像的分类识别。在真实的车标图像数据集上的实验结果表明,IC-ACO算法比其他类似算法具有更高的分类识别率。     

基于近邻传播聚类的集成特征选择方法

针对高维数据中的类标记仅与少部分特征关联紧密的问题,提出了基于排序聚合和聚类分组的特征随机选择集成学习方法。采用排序聚合技术对特征进行过滤,选出与样本分类相关的特征,以bicor关联系数作为关联衡量标准,利用近邻传播聚类算法进行分组,使不同组的特征互不关联,然后从每个分组中随机选择一个特征生成特征子集,便可得到多个既存在差异性又具备区分能力的特征子集,最后分别在对应的特征子空间训练基分类器,采用多数投票进行融合集成。在7个基因表达数据集上的实验结果表明,提出的方法分类误差较低,分类性能稳定,可扩展性好。     

基于热度曲线分类建模的微博热门话题预测

及时掌握大众关心的热点话题是企业进行商业创新和商务营销的重要前提。现有方法大都依赖于非结构化数据的处理或反复遍历样本集,使算法复杂性较高。文中从话题的统计特性出发,提出建立在结构化数据上的非参数方法。首先对单个话题构建表征话题传播扩散程度和关注聚焦程度的热度曲线;然后对这些形态丰富的热度曲线进行分类建模,得到不同类别曲线的共性特征及发展规律;最后使用分类模型上的加权投票规则预测新话题是否会发展成为热门话题。基于新浪微博平台进行数据收集和实验,结果表明该方法数据结构简单、效果良好、复杂度低且易于控制。